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• Combined modal-split assignment model with autonomous taxis (ATs) was developed. • Policy analysis was conducted in Nagoya considering fare, delay, and dedicated lanes. • Trips using of taxis will increase by a factor of 11 when ATs are adopted. • Rail usage will decrease by 1.5%, while usage of other modes will decrease by 4%–5%. In recent years, the implementation of autonomous vehicles has been widely discussed worldwide. In particular, urban transportation demand is expected to change significantly when autonomous taxis (ATs) are introduced. Thus, planners must anticipate changes in traffic conditions and the number of users of other transport modes. Therefore, changes in travel behavior and traffic conditions must be quantified with respect to comprehensive changes in the service level of ATs, including changes in fares and the possibility of delays. However, previous studies have not sufficiently considered factors such as the intention to use ATs, the interrelationship between mode choice and traffic congestion, and the impact of ATs on public transit. Therefore, they are not applicable to city-level transportation demand forecasts. The purpose of this study was to propose an easily implementable method for forecasting urban transportation demand when AT services are adopted, which overcomes such problems. In this study, we developed a combined modal split-assignment model and analyzed the effects of various AT service implementation scenarios in Nagoya, Japan. The results showed that the number of trips using taxis will increase by a factor of 11 when AT services are implemented. Additionally, the usage of other modes is expected to decrease by 4%–5%, except for rail usage with a decrease of 1.5%. [ABSTRACT FROM AUTHOR]

• Employs quantitative research and analysis methods. • Explores traffic congestion in light of CSR ratings and its translation to real-world outcomes. • An innovative idea on sustainable transportation and policy in CSR. • Enhancing CSR policy in mitigating traffic congestion is an area where private entities can have a significant local impact on a social issue affecting people, plant, and profit. Traffic congestion (TC) is a complex issue that has an adverse impact on the environment, business operations, livability, and health of a community. Supply-side TC mitigation measures increase transportation capacities while demand-side measures attempt to modify travel behavior so that the travel demand is reduced. As part of the demand-side, some corporations are investing in TC reduction through actions that contribute to improved walkability, reduced peak travel demand, and active commuting. While beneficial for their local communities, TC reduction efforts are not specifically tagged as a part of corporate social responsibility (CSR) endeavors. CSR involves reporting of a company's stewardship towards its community and environmental impact. Research reflecting on the impact of CSR on TC has not been conducted. This study aims to fill this gap. Using corporations headquartered in the top traffic-congested cities in the United States, this study examines the relationship between TC and CSR. This research employed a hierarchical linear model with two datasets, Travel Time Index (TTI) and CSRHub ratings. Of the four CSR categories studied, community, employees, and environment ratings are significantly related to TTI, with employees and environment ratings having an inverse relationship to TTI. This shows that congestion has a strong impact on the environment and that companies, through their employee policies, can impact TC. The results also highlight the opportunities that companies have in potentially reducing their environmental impact by incorporating congestion reduction strategies as part of their CSR, either as a separate measure or as part of their environmental or employee CSR ratings. Our results are also a starting point for new tools/strategies that transportation policymakers and analysts can use to engage companies to help mitigate TC. A further study on other cities with major traffic problems may shed more light on CSR and TC. [ABSTRACT FROM AUTHOR]

Since the 1970s, high-occupancy vehicle (HOV) lanes have been a common policy instrument to mitigate traffic congestion. Yet, their effectiveness remains a controversial topic among researchers, policy makers, and the public. In this debate, a key unknown has been the impact of HOV lanes on commuters' carpooling behaviors. This paper brings a new piece of evidence by offering a data-driven assessment of carpooling intent and adoption, using revealed-preferences data. We partner with Waze, a major carpooling platform, and leverage a natural experiment following the introduction of three HOV lanes in Israel in 2019. Using tailored treatment and control groups coupled with econometric analyses, we derive four main findings. First, HOV lanes bring new users to the carpooling platform, which contributes to alleviating the "cold-start" problem in the marketplace. Second, HOV lanes have a positive impact on carpool intent: the number of carpool offers sent by drivers increase manifold following the introduction of the HOV lanes. Third, HOV lanes have a disparate impact on carpool adoption: carpools increase significantly for two out of three HOV lanes. This result underscores the critical impact of HOV lanes design: it seems more beneficial to have round-trip HOV lanes (as opposed to one-way lanes) and two-passenger occupancy requirements (as opposed to three-passenger requirements). Last, HOV lanes have a broader impact, by increasing carpooling on non-HOV routes and shifting the travel behaviors of non-carpoolers. We conclude by discussing policy implications, highlighting collaboration opportunities between policy makers and digital carpooling platforms to enhance the design and operations of HOV lanes. [ABSTRACT FROM AUTHOR]

• Vehicle restriction improves public transport ridership and ride-hailing demand. • Short-term and long-term effects on various travel modes are discussed. • Traffic conditions are much better, although vehicle reduction is less than expected. • Multisource mass data of various travel modes are utilized. Vehicle restriction policies are introduced worldwide to alleviate traffic congestion and promote public transportation trips. Many cities in China have been taking the one-day- per -week (ODPW) restriction policy as a regular traffic management measure, while in special cases, the stricter odd-and-even (OAE) vehicle restriction policy is implemented for contingency. However, empirical evidence on the impact of these restrictions on various transportation modes in cities remains lacking. By using multisource mass data of various travel modes in Zhengzhou, China, this study investigates the practical effects of strict vehicle restriction policies from two perspectives of resident trip characteristics, namely, travel demand of various travel modes (subways, buses, and ride-hailing services) and road network traffic conditions. Results highlight a significant increase in the travel demand of alternative travel modes as the policy shifted from ODPW to OAE, with ride-hailing orders enduring a greater impact than public transportation. The long-term impact of these policies on these travel modes, except for buses, tends to be moderate. While traffic conditions have significantly improved as a result of these policies, their effects on reducing the number of vehicles on the road are less than expected. This study contributes to a comprehensive interpretation of the effects of strict vehicle restrictions and provides insights for making rational policy decisions. [ABSTRACT FROM AUTHOR]

One of the core reasons for urban traffic congestion is the mispricing of the traffic's main resource, the scarce road capacity. Recent proposals suggest a two-stage market for road capacity similar to other major utilities markets such as those for electricity. In such markets an Independent System Operator (ISO) provides road infrastructure which multiple Service Providers (SP) then resell to end consumers. The size of such markets in terms of the number of traded items, road segments per hour, is exceptional. A road network in a city consists of tens of thousands different road segments and it is unclear how a wholesale market for service providers should be designed to keep it tractable for buyers and sellers. Three problems arise in this context: First, how can a service provider express preferences for so many products. Second, the optimization problem is very large and it is far from obvious that problems of this size can be solved. Finally, the allocation problem is non-convex and finding competitive equilibrium prices on such markets is impossible in general. We suggest a parsimonious bid language based on origin–destination pairs and introduce a mixed-integer optimization problem to maximize welfare on such markets. Based on the MATSim traffic simulator, we show that one can solve realistic problems based on traffic data for the city of Berlin to optimality within 15 minutes. Importantly, we approximate competitive equilibrium prices which require only negligible side-payments as the allocation problem is "almost" convex. Overall, the paper provides a tangible proposal for wholesale markets for road capacity. [ABSTRACT FROM AUTHOR]

• A mixed-methods approach is conducted to investigate the perceived resilience. • Information transparency, compensation and reward, and usage flexibility are cues. • Cues contribute to the perceived diagnosticity, trust, and perceived resilience. • Individuals' trust positively impacts their perceived resilience. • Time urgency plays a crucial moderating role. Ridesharing plays a potential role in supporting transportation systems. However, the impact of service cues on individuals' perceptions of transportation system resilience has yet to be fully examined. Building on cue utilization theory, this study adopted a mixed-methods approach to investigate the influence of congestion relief services provided by ridesharing platforms on individuals' perceived resilience and trust in the road transportation system and the impact of passengers' time urgency in this context. In Phase 1, semi-structured interviews were conducted to identify the cues derived from ridesharing platforms and those utilized by ridesharing users during the processes of calling and waiting for a ride. Subsequently, a research model and hypotheses were proposed. In Phase 2, path analysis was performed to validate the model. Results revealed that the intrinsic and extrinsic cues during the calling and waiting processes enhance individuals' perceived diagnosticity, further promoting their trust and perceived resilience in the road transportation system. Moreover, as individuals' trust in the road transportation system increases, their perceived resilience increases. Additionally, passengers' time urgency negatively moderates the positive relationship between usage flexibility and perceived diagnosticity. Overall, this study enriches existing literature and provides practical implications for traffic management departments in mitigating uncertainty in the road transportation system. [ABSTRACT FROM AUTHOR]

Vehicle ownership restriction (VOR) is considered effective in reducing traffic congestion and air pollution. However, citizens manage to circumvent VOR by registering vehicles from other cities, undermining the effectiveness of VOR. To close the loophole, some cities introduced a non-local vehicle restriction (NLVR) policy with an announcement period prior to the official enforcement of NLVR. By taking Beijing, China as a case study, this paper quantifies the effectiveness of NLVR in patching VOR and explores citizens' adaptive behavioral response during the announcement period. A propensity score matching with difference-in-differences (DID) method is first adopted to quantify the new vehicle registrations in neighboring cities of Beijing. Then, a regression control method based on Lasso and a DID approach are used to isolate the causal effect of NLVR on air pollution. Finally, we investigate the announcement effect of NLVR on new vehicle registrations and air pollution, respectively. Our results suggest that NLVR has its intended effect of closing the loophole in Beijing's previously-enacted VOR. Average monthly new vehicle registrations in neighboring cities of Beijing drops by 3.4% and NO 2 concentration in Beijing decreases by 5.59 μ g / m 3 . During the announcement period, we observe a significant decrease in new vehicle registrations in neighboring cities, suggesting that potential buyers have refrained from purchasing new vehicles in anticipation of the forthcoming stringent NLVR. However, there is no notable decline observed in NO 2 concentration in Beijing. We recommend that policymakers should fully analyze the relationship between participants, foster regional cooperation on transport policies, and design a reasonable announcement period according to the policy's purposes. • Non-local vehicle restriction effectively closes the vehicle ownership loophole. • DID results show reduced new vehicle registrations in neighboring cities. • Potential buyers abstain from purchasing new vehicles before policy enforcement. • No notable NO 2 decline in Beijing during the announcement period. • Employing RCM based on Lasso, a cutting-edge policy evaluation method. [ABSTRACT FROM AUTHOR]

• Road pricing is a potential tool for controlling traffic congestion and pollution. • We study cordon tolls using the dynamic traffic network simulator METROPOLIS. • A Gaussian dispersion model is used to compute the distribution of pollution. • Step tolls have only a small advantage over flat tolls for pricing emissions. • Health effects vary with location relative to the cordon(s) and wind direction. Road traffic is a major contributor to air pollution which is a serious problem in many large cities. Experience in London, Milan, and Stockholm indicates that road pricing can be useful in reducing vehicle emissions as well as congestion. This study uses a dynamic traffic network simulator that models choices of mode, departure time, and route to investigate the effectiveness of tolls to target emissions and congestion externalities on a stylized urban road network during a morning commuting period. The spatial distribution of four pollutants is calculated using a Gaussian dispersion model that accounts for wind speed and direction. Single and double cordon tolls are evaluated, as well as flat tolls that do not change during the simulation period and step tolls that change at half-hourly intervals. The presence of emissions externalities raises optimal toll levels, and substantially increases the welfare gains from tolling, although the proportional advantage of step tolls over flat tolls is lower than if congestion is the only externality. The individual welfare-distributional effects of tolling vary strongly with residential and workplace locations relative to the cordon, and also differ for the upwind and downwind sides of the city. [ABSTRACT FROM AUTHOR]

Buses are the mainstay of public transport systems in many cities but are typically subject to significant delays due to traffic congestion. We examine the welfare effects of providing dedicated bus lanes in the city of Rome, Italy. We demonstrate that a dedicated bus lane reduces bus travel time by about 18 percent. Our welfare analysis focuses on the situation where mixed road lanes are turned into dedicated bus lanes. We find that bus lanes are undersupplied, despite the additional time costs due to reducing road capacity available to cars. [ABSTRACT FROM AUTHOR]

This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model that describes the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model that estimates the TNC's level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and solve the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to regulatory costs and/or constraints. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC's profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC's level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve. [ABSTRACT FROM AUTHOR]

Online shopping has shown a remarkable growth trend for the past three decades. The increasing demand for deliveries resulting from online shopping generates lots of negative externalities contributing to traffic congestion, parking violations, and air pollution. To mitigate these negative externalities, the importance of an alternative delivery location (ADL) is growing in transportation area as delivery and transportation systems are intertwined. The ADL includes a delivery locker (e.g., Amazon Hub Locker), a brick-and-mortar store who partners with parcel delivery companies (e.g., UPS Access Points), and a postal store. Despite the growing size and importance of the ADL, little is known about receivers' behavior on the ADL from the transportation perspective. To better understand the ADL, this research developed joint models of parcel demand and the ADL to investigate factors affecting (a) the adoption of the ADL and (b) the distance people are willing to travel using the NYC DOT Citywide Mobility Survey in 2018. The results show that delivery frequency received by respondents negatively affects the adoption but does not affect the tolerable distance. The impacts of socio-demographic attributes on the behavior are also discussed. A prediction to the entire population in Manhattan was made and a corresponding strategy to determine the optimal ADL location is discussed. This research is the first attempt comprehensively investigating behaviors on the ADL from the transportation planning perspective. This research will contribute to the literature in urban freight systems by improving understanding of the ADL in dense urban areas. [ABSTRACT FROM AUTHOR]

Evacuation during a catastrophic disaster is a crucial operation that needs to be appropriately managed and is of more importance when considering the elderly and people with disabilities. The uncertain and unpredictable nature of disasters can cause long-term repercussions, especially in traffic congestion. This study presents a mathematical model to formulate traffic balance for regular and assisted evacuation (that is, disabled and the elderly) whilst considering traffic congestion in evacuation clearance time by applying contraflow. A Branch and Price (B&P) related approach is developed to help solve the proposed model in large-size problems. The presented algorithm is applied to a case study of Australia's 2013–14 bushfires in Gippsland, located in the eastern part of Victoria. A variation test is performed to evaluate the robustness of results generated by the developed model. Results indicate that the participation percentage of edges is different based on their location, capacity, and sustainability of blockage. The edges' capacity influences the evacuated population most compared to route capacity and time window. The output of this approach enables authorities to improve the resilience of communities by making optimal strategic and operational decisions for enhancing an evacuation response as well as influencing appropriate policies. • A novel integrated assisted mass evacuation model is developed to improve efficiency. • Evacuation of elderly people and those with disabilities are investigated in this study. • An Accelerated Branch & Price algorithm is proposed to tackle large-scale evacuation issues. • Contraflow is added to the model to boost network capacity and cut clearance time. [ABSTRACT FROM AUTHOR]

• This study conducted a laboratory experiment in a mixed traffic with autonomous vehicles (AVs) and manually-driven vehicles (MVs) to uncover travel mode preferences. • The result showed that more than half of the travelers chose AVs. The result came close to Nash equilibrium, but did not reach social optimum. • Those who were provided more information will consider the cost difference between the two modes, which resulted in losses of both individual and social benefits. • Subsidies that eliminated the cost inequality can make the system more efficient. The effect of information on social welfare depended on whether providing subsidy or not. • Learning model simulation showed that the main factors affecting travelers' decisions were inertia, experiences, and inequality. An increase in autonomous vehicles (AVs) would result in a decline in traffic congestion; however, the travel cost associated with AVs is always higher than that of manually-driven vehicles (MV). This situation is interpreted as a so-called multi-player social dilemma. This study designed an economic experiment to investigate the effect of AVs on mode choice in mixed traffic flows. Participants were informed about the cost function in both modes and were asked to choose the travel mode for more than 60 rounds. In full information (FI) treatment, participants received information about the travel costs of the AV and MV modes at the end of each round. In the partial information (PI) treatment, participants received information only about the travel cost of the mode they chose. We found that participants were sensitive to cost differences in the FI treatments. Based on inequality aversion models, we proposed a perceived cost that could better explain the experimental equilibrium. A monetary reward was provided to encourage participants to take AVs and solve social dilemmas. The results demonstrated that the reward mechanism reduces traffic congestion and increases social benefits, especially in the FI treatment. Finally, a learning model that considers inertia and perceived cost is proposed to explain the decision-making process of the participants during the experiment. The findings have implications for traffic forecasting in the mixed flow of MVs and AVs and provide insights and policy suggestions for AV management. [ABSTRACT FROM AUTHOR]

• Owning a car is not related to increased life satisfaction in Beijing. • However, owning more than one car weakly contributes to higher life satisfaction. • A more expensive car does not influence travel and life satisfaction. • Infrequent car use is related to higher travel and life satisfaction. • Attitudes towards cars confound the effect of car ownership and use on well-being among car owners. Promoting subjective well-being is an important goal in urban policy. Transportation scholars increasingly evaluate transportation systems based on people's subjective well-being. This study joins this strand of efforts to answer the following research question: how do constrained car ownership and car use influence travel and life satisfaction? Empirical data were collected in Beijing where various policies had been implemented to curb the rapid growth of car ownership and mitigate traffic congestion. We answer the question by examining the effects of car ownership and car usage variables on travel and life satisfaction. Our study shows that owning multiple cars increases vehicle users' life satisfaction, but owning a car is not related to increased life satisfaction. Purchasing a more expensive car and driving cars more frequently in daily life could not increase people's life satisfaction, but infrequent car use contributes to higher travel and life satisfaction. Among vehicle users, using cars to commute is related to higher travel satisfaction but not higher life satisfaction. In contrast, using cars for non-work trips is related to higher life satisfaction but not travel satisfaction. We also find that attitudes towards the instrumental and affective roles of cars are positively related to travel and life satisfaction among car owners. Results of this paper suggest the importance of promoting sustainable transportation and behavioral education in cities like Beijing. [ABSTRACT FROM AUTHOR]

• Conduct a revealed preference (RP) questionnaire survey in Xi'an, China. • Apply the PLS-SEM in addition to the CB-SEM to analyze the effect of latent factors on public acceptance of the LRP policy. • Find the main factors affecting public acceptance of the LRP policy. • Provide the recommendations for policy makers and policy implementers. Over the last decade, there has been a steady increase in the number of cities in China and around the world adopting License Plate-based Restriction (LPR) policies, whereby vehicles are permitted to enter a defined road or area on the basis of a specific digit on the registration, license or number plate, to help mitigate traffic congestion and its associated impacts. Although the costs and benefits of implementing a LPR policy have been recognized, the public acceptance of a LPR policy has been less well studied. To better understand the factors affecting the public acceptance of a LPR policy, we improved the theoretical model by adding two factors related to attitude and new supposed deeper level effects between different factors. Accordingly, the theoretical model on exploring the factors affecting the public acceptance of such a scheme were explored in a questionnaire completed by 619 local residents in Xi'an, China and analyzed using a partial least square structural equation model (PLS-SEM). In addition, we carried out heterogeneity analysis to compare the differences between residents with various socio-economic attributes in factors with respect to affecting the acceptance of a LPR policy. The results show that problem perception, perceived effectiveness, perceived value and social norms exert a significant direct and indirect impact on the acceptance of a LPR policy. Important aims to reach can only indirectly affect the acceptance of a LPR policy by perceived effectiveness and perceived value, while policy cognition, attribution of responsibility have no significant influence on the acceptance of a LPR policy. Important aims and perceived effectiveness directly and direct affect the acceptance of a LPR policy by residents who are young, high-income and car-owning. The findings of this study can assist policy makers to design a more acceptable policy to further mitigate congestion situations. [ABSTRACT FROM AUTHOR]

• We re-examine the controversial issue about whether or not high-quality bus service can significantly reduce car use while increasing bus use. • A SEM model is constructed that contains bus service quality, car ownership, bus use and car use. • Improving bus service quality can indeed reduce car use by attracting more travelers to the bus. • The extent of reducing car use by improving bus service quality is far less than that of increasing car use caused by car ownership. The State Council of China hopes to alleviate traffic congestion by implementing the bus priority policy. However, the policy consequences have not been satisfactory. Whether a good bus service can significantly reduce car use while increasing bus use is a confusing issue, hereto this study re-examines the interactions among bus service quality, car ownership, car use and bus use by constructing a SEM model. A sample of 334 individuals in Changzhou is used in the study. Results show that although high-quality bus service can cause a decrease in car use, the total negative effect of bus service quality on car use is far less than the positive effect of car ownership. Thus, when car ownership is growing rapidly, only a comprehensive and substantial improvement in bus service can alleviate and ultimately eliminate the enormous pressure on urban traffic resulted from the increasing car ownership. This study provides empirical evidence to explain why traffic congestion is still serious with the strong support in public transport from governments and gives policy implications on how to control urban traffic. [ABSTRACT FROM AUTHOR]

Where traffic is congested and all the curb spaces are occupied, some drivers are probably cruising for parking. Cities can eliminate this cruising by charging demand-based prices for curb parking to ensure one or two open spaces on every block. Drivers will then usually find convenient places to park within a short walk of their destinations. But if cities charge demand-based prices for curb parking, how will drivers choose where to park, and what will they pay? To answer these questions, I have examined how four variables—parking duration, number of persons in the car, walking speed, and value of saving time spent walking—determine parking choices when prices increase as drivers approach their destinations. Short-term parkers, carpools, slow walkers, and drivers with a high value of saving time will park closer to their destinations. Long-term parkers, solo drivers, fast walkers, and drivers with a low value of saving time will park farther away. This spontaneous, self-organizing pattern of parking choices responding to demand-based parking prices will minimize the collective cost of the time drivers spend walking to and from their destinations. Demand-priced curb parking will also reduce uncertainty about travel times. Drivers delayed by traffic congestion can save time at the end of their trips by paying a higher price to park closer to their destinations. Demand-priced curb parking at the end of a trip can serve as a buffer allowing late-arriving drivers to buy time when they need it most. [ABSTRACT FROM AUTHOR]

This paper addresses the question, "Will post-pandemic travel behaviour, when the virus has gone, be different to pre-pandemic travel?". It adopts an online survey where respondents were asked to report changes in travel during the various stages of the pandemic and expectations of future travel after the virus has gone. The paper focusses on commuting including total commuting, work from home (WFH), employment, travel mode volume and share and timing of morning commute trips using reported behaviour during pandemic shutdowns; and expectations of commuting when the virus has gone. The paper provides evidence that travel behaviour post-pandemic might be different to pre-pandemic travel. It suggests that after the pandemic, public transport ridership, which declined steeply during the pandemic, will return but not to pre-pandemic levels. A post-pandemic reduction effect of around 20% in transit commuting is expected. This effect is supported using secondary evidence from a number of international cities. Results imply a mode shift from public transport use to car driving; this will be particularly large for CBD/downtown areas and is likely to result in peak period traffic congestion after the virus has gone. Work from home increased substantially during the pandemic; this will reduce after the pandemic as enforced WFH is replaced by voluntary WFH. Nevertheless, a sustained future ongoing increase in WFH above pre-pandemic levels is suggested, acting to reduce peak commuting by 6% and commuting to Melbourne CBD by 20%. However, reductions in commuting due to WFH do not offset mode shift from public transport to car driving resulting in a net increase in car use after the pandemic. Infection fear is a new top concern of public transport users since the pandemic. This fear has transitioned from 'fresh infection fear'; the initial concerns when the pandemic started to 'residual infection fear'; a long-term effect when the virus has gone. Implications of the findings for research and practice are discussed. [ABSTRACT FROM AUTHOR]

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially. [ABSTRACT FROM AUTHOR]

Pedestrianized zones are becoming an increasingly popular policy tool for cities seeking to improve "walkability", "livability", and reduce congestion in the urban center by prohibiting automobiles from entering a designated zone. However, pedestrianized zones can be inappropriately implemented in locations without justifiable congestion or sufficient transit alternatives. For mixed-traffic transit (e.g., bus and light-rail streetcars), the lack of reasonable transit alternatives is an issue further compounded by the traffic congestion generated from diverted driving trips concentrated around the pedestrianized zone, inadvertently slowing down both automobile traffic and mixed-traffic transit in tandem. Alternatively, to mitigate the traffic congestion impact on transit, policy makers can also designate a "transit priority" zone around the pedestrian zone, where transit is unimpeded by congestion either through transit signal priority, dedicated lanes, or both. This ensures transit provides a consistent travel alternative as congestion varies, creating a stable demand equilibrium that would otherwise be suppressed by congestion. While such a solution has been proposed in advocacy, no research has explored such a combined policy in analytical terms. This research explores the potential complementary benefits and optimal sizing of pedestrianization and transit priority zones through analytical evaluation in an idealized rectilinear city. The model is intended to provide insights regarding traffic impacts, optimal zone sizing, system capacity, and the justifiable thresholds for implementing complementary pedestrianization and transit priority zones in a city. [ABSTRACT FROM AUTHOR]

An agent-based, multichannel simulation of a downtown area reveals the impacts of both time-shifting traffic demand with congestion pricing, and supplying extra capacity by banning left turns. The downtown street network was idealized, and loosely resembles central Los Angeles. On the demand-side, prices were set based on time-of-day and distance traveled. On the supply side, left-turn maneuvers were prohibited at all intersections on the network. Although both traffic management measures reduced travel costs when used alone, the left-turn ban was much less effective than pricing. When combined with pricing under congested conditions, however, the left-turn ban's effectiveness increased considerably—it more than doubled in some cases. Furthermore, the two measures combined reduced travel costs in synergistic fashion. In some cases, this synergistic effect was responsible for 30% of the cost reduction. This strong synergy suggests that turning bans should be considered as an added option when contemplating congestion pricing. [ABSTRACT FROM AUTHOR]

Activity resource allocation or dispersion (e.g., employment, education), transport network improvement, and property development, affect the urban community in different ways. To illuminate their intricate relations, we study the perspectives of three groups of stakeholders: i) households with heterogeneous members seeking a common residence to optimize their proximity to activity resources; ii) transport planners aiming to relieve traffic congestion; and iii) property developers aiming to maximize their housing profit through the bid-rent process. To assess the impacts of different resource allocation patterns (centralized versus dispersed), a household bid-rent and residential location choice model with heterogeneous compositions is developed, including household members' activity destination choices and travel choices under stochastic user equilibrium. We apply this model to a new redevelopment region in Kowloon, Hong Kong. The results show that centralized resource allocation increases the producer surplus but reduces the household disposable income. Compared with dispersion in school resource allocation or transport improvement, job reallocation is the most efficient in alleviating traffic congestion as employments are dispersed into multiple centers, and inter-zonal commute trips are converted into intra-zonal trips. We also investigate the outcomes as the urban structure varies from monocentric, dual-core into multiple self-contained communities (i.e. polycentric). The results show that any accessibility gain due to a change in the urban form will be capitalized by developers into higher housing prices in the new central business district (CBD) regions, hence shrinking the consumer surplus and disposable income. Therefore, it is unlikely to find an urban form that will achieve win–win–win situations for all three stakeholder groups in the case study example. [ABSTRACT FROM AUTHOR]

In response to severe traffic congestion and air pollution, Beijing introduced a car ownership restriction policy to curb growth in the number of private cars in the city. However, Beijing residents can still purchase and register their cars in neighboring cities and this "leakage" may substantially reduce the policy's effectiveness. Using city-level data collected from the CEIC China Premium Database, we aim to quantify the spill-over effect: the impact of Beijing's policy on the growth of private car registrations in neighboring cities. We first deploy a synthetic control method to create a weighted combination of non-treated cities for each treated city. We then employ a difference-in-differences approach to estimate the policy leakage. Our models suggest that the policy resulted in additional 443,000 cars sold in the neighboring cities (within 500 km of Beijing) from 2011 to 2013, compared to if the policy had not been implemented. 35–40% of the car growth reduction stipulated by the policy simply spilled over to neighboring cities. The significance of the policy leakage necessitates positioning Beijing's urban transportation in a broader context and executing regional collaboration. [ABSTRACT FROM AUTHOR]

• The effect of fuel prices on traffic flows in New South Wales is studied. • The dataset consists of 11.9 million observations. • The average gasoline price elasticity of hourly traffic flows is −0.04. • The gasoline price elasticity of weekday peak-period traffic flows is positive. • Higher fuel prices lead to faster travel speeds in peak afternoon periods. Understanding how traffic flows respond to fuel price changes is useful for traffic management. This study uses a dataset of 11.9 million hourly observations from 118 traffic count stations over 2010–2017 to investigate the relationship between gasoline prices and traffic flows in the state of New South Wales, Australia. The findings suggest that higher gasoline prices reduce traffic flows, with an average effect size of −0.04 in the hourly estimates. The elasticity is particularly pronounced during off-peak periods, both on weekdays (−0.10) and weekends (−0.07). In contrast, a positive effect of gasoline prices on traffic flows is observed for peak periods on weekdays (0.06). Evidence is also obtained that afternoon peak-hour speeds are faster when gasoline prices are higher, consistent with a lowering of traffic density. The research also finds a negative price elasticity of gasoline demand and that people are more likely to use public transport when gasoline prices are higher. The findings suggest that fuel excise plays a role in both reducing overall road dependence and alleviating the severity of some peak-hour traffic jams. [ABSTRACT FROM AUTHOR]

Variable message signs (VMS) have been widely applied to alleviate traffic congestion by providing spatiotemporal information related to traffic and network conditions to drivers. There is a debate on whether the VMS information would lead to the less-alleviation of congestion or congestion transfer to the adjacent roads in the different traffic congestion situations due to the inappropriate VMS information. Additionally, it is still unknown how to find a practically applicable method to design the most effective VMS information for drivers. In order to design and display the appropriate VMS information for drivers under different traffic conditions, this paper aims to identify the effects of VMS on individuals' route choice behavior by exploring the different effects of VMS on the several separate groups. Drivers in these groups are homogenous in gender, driving experience, education level, occupation, and monthly income. In the hypothetical scenario, a stated preference (SP) questionnaire was conducted to build the dataset consisting of 3462 samples from local private and taxi drivers. The decision tree via the chi-squared automatic interactive detector (CHAID) algorithm is utilized to split drivers into several groups, and further two fixed-parameter logistic regression models considering intra-heterogeneity between drivers (i.e., considering and not considering systematic taste variations), and random-parameter logistic regression models are employed to analyze the factors affecting driver route choice behavior of each group. Five groups with different driver characteristics are identified. Besides, the modeling results indicate that the effect of the VMS information on individual route choice behavior is heterogeneous. These findings include: (i) random-parameter logistic regression models have a significantly improved goodness-of-fit because of the lower AIC and BIC; (ii) compared with the stated cause information on road construction and congestion, the accident information had a larger effect on driver route choice behaviors; (iii) the route choice behaviors of drivers with more than 6-year driving experience are affected by the VMS information on road constructions, accidents, and the quantitative delay time; (V) the route choice behaviors of less-experienced female drivers were affected by display formats and display frequencies of VMS information. The findings from this study can assist traffic authorities in designing the most appropriate VMS for different traffic congestions based on driver characteristic distribution and the road capacity for improving the practicability of VMS information and further provide the theoretical evidence for the design of in-vehicle personalized information service systems. [ABSTRACT FROM AUTHOR]

Public bicycle sharing system (PBSS) has been adopted around the globe to reduce traffic congestion, CO 2 emissions and for positive health effects. The first system was initially started in the year 1965 at Amsterdam and now it has been expanded almost in all the continents of the globe. The adoption of this concept was initially seen in developed countries. Now in the past decade various developing countries also have implemented the PBSS in their urban areas. However, the developing countries are facing many implementations and less ridership issues due to various barriers. This paper identifies the barriers which are hinders in the way of implementation of PBSS. An identified 31 barrier was categorized under six groups for ranking. The Fuzzy Analytical Hierarchical Process (FAHP) is applied to examine the relevance and prioritize them with the help of different stakeholders. Total 31 stakeholders were selected as experts from different working backgrounds for this study. The experts were grouped into six namely decision makers, academicians, consultants, scientists and researchers, operators, and users. The perspective of each group is also examined and checked using Kendall' coefficient. The majority of experts opine that City infrastructure barrier and travel characteristics barriers are most influential barriers for successful implementation of PBSS while technological and geographical barriers are least influential barriers. The overall results and opinions by different group of experts will help the decision makers to formulate effective policy to tackle all the barriers prior to implementation of PBSS in Indian urban areas. With the help of this framework, time and financial resources of the government can be utilized in an optimum way and the savings can be utilized for improving necessary city infrastructure. [ABSTRACT FROM AUTHOR]

• A scaled mixed logit model was used to estimate the travel preferences in Beijing. • A semi-random sample of 2652 respondents participated in the experiments. • Many findings deviate from results obtained in developed countries. • Travelers in Beijing are much less sensitive to possible delays caused by traffic congestion. Metropolises in emerging markets are facing serious urban transport challenges. Understanding people's travel preferences is crucial for designing effective sustainable urban policies. Little attention has been paid to studying travel preferences in multimodal transport systems in these markets. This study estimates the travel preferences in the metropolitan area of Beijing, which is notoriously plagued with high degrees of congestion. We administered a series of interwoven stated preference experiments on travel behavior. A semi-random sample of 2652 respondents participated in the experiments. The data were pooled and a scaled mixed logit model was used for estimation. The results provide rich information on how trade-offs are made among the manipulated attributes regarding travel time, cost, convenience, and reliability. Many findings deviate from results obtained in developed countries. A contrast standing out is that travelers in Beijing place much less weight on possible delays caused by traffic congestion. [ABSTRACT FROM AUTHOR]

• The time-varying effect of pricing on on-street parking behavior is investigated. • Field-collected panel data over four time periods is used. • Parking duration decreases as parking price increases. • The elasticity of parking turnover fluctuates during the study period. Parking pricing is considered a powerful tool for improving parking management and relieving traffic congestion in metropolitan areas. This study uses field-collected panel data over four time periods before and after the implementation of a new parking pricing policy in Nanning to investigate the time-varying effect of pricing on on-street parking characteristics, including parking duration and parking turnover. Results show that parking duration decreases as parking price increases, with the relationship presenting a growing elasticity. The elasticity of parking turnover fluctuates due perhaps to the influence of vehicle ownership. Kolmogorov–Smirnov (K-S) tests show that on-street parking characteristics differ significantly before and after the implementation of a new parking pricing structure. Strategies that can improve parking services are also investigated based on the empirical data and modeling results. [ABSTRACT FROM AUTHOR]

• A machine-learning approach is used. • The traditional FCM clustering algorithm is improved in terms of the fuzzy membership function and weighting processing of the samples. • The ample degree of road network is introduced in this paper. • A comprehensive analysis of classification accuracy is conducted to evaluate the classification performance. The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk. [ABSTRACT FROM AUTHOR]

Inferring travel modes of travelers in the city is important to transportation planning and infrastructure design. Based on the distribution of travel modes, transportation engineers could provide some proper strategies to reduce traffic congestion and air pollution. With advanced sensing techniques, it is possible to collect high-resolution GPS trajectory data of travelers and we can infer travel modes using some popular machine learning methods. One of the difficult tasks facing the application of machine learning especially deep learning in travel mode detection is the lack of large, labeled dataset, because to label the trajectory data is expensive and time-consuming. Moreover, samples of different travel modes are always unbalanced. Accordingly, in this paper, we take advantage of the generative model and the Convolutional Neural Networks (CNN) to develop a hybrid travel modes detection model using less labeled trajectory data. Our key contribution is the utilization of a generative adversarial network (GAN) to artificially create some training samples in such a way that it not only increases the required sample size but balances the dataset to improve the accuracy of the detection model. Furthermore, CNN is applied to extract deep features of trajectory data, and then to classify the travel modes. The results show that the highest accuracy (86.70%) can be achieved by the proposed model. In particular, the proposed method can improve the detection accuracy of bus and driving modes because it can solve the small sample size problem. Moreover, the large sample size can provide an opportunity to develop some advanced deep learning models in future studies. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator's profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator's side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users' side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both , the SAV and CC mode in order to improve a city's transport system. [ABSTRACT FROM AUTHOR]

Public transport strikes comprise a form of traffic disruption affecting "normal" traveller behaviour, and result in the increase of traffic congestion, traffic accidents and air pollution. Travellers modify their trip characteristics in anticipation of strike effects, while their behaviour depends on various factors including individual characteristics, trip flexibility, available transport modes and attitudes towards them. This study explores the effect of public transport strikes on traffic conditions utilising loop detector data and, in particular, examining the impacts on traffic flow, mean speed and travel time. The city of Athens is used as a reference case. The analysis employs descriptive statistics, as well as the design of generalised linear models to capture the impact of public transport strikes on traffic conditions, and to define the contributing spatio-temporal factors affecting the impacts. Study results highlighted the increase of congestion as expressed through all explored indicators, as a result of the strikes. In particular, increased traffic flow (up to 30%), reduced mean speed (up to 27%) and increased travel times (up to 25%) were observed at the majority of the explored sites. Public transport strike coverage and time of day were found to be the dominant factors. Other contributing factors were found to be day of the week and site-specific characteristics including: the existence of tolls; site location relative to the centre of Athens; and the existence of traffic lights on the explored links. The results of this study can form the basis for the design and implementation of targeted countermeasures that mitigate congestion effects, as well as measures promoting green transport resulting in travellers adopting more sustainable trip routines in the city of Athens. [ABSTRACT FROM AUTHOR]

• Noncompliance behavior against vehicle restriction policy is analyzed. • Empirical data and quantitative models are used to investigate the behavior. • Trip characteristics show statistically significant effects on the behavior. • Methods for preventing noncompliance and improving the policy are discussed. To alleviate traffic congestion, vehicle restriction policies have been widely used globally. However, noncompliance behavior undermines the effectiveness and sustainability of such policies. This study focuses on investigating the characteristics of noncompliance behavior under two vehicle restriction policies, namely, one-day-per-week (ODPW) and odd-and-even (OAE) policies. On the basis of license plate recognition data that record the traffic condition under ODPW and OAE policies in Langfang, China, descriptive statistics are used to illustrate the characteristics of noncompliance with the policies. Furthermore, a zero-inflated model is used to determine the manner in which influencing factors (i.e., local versus external indicator, unrestricted travel rate, daily travel distance within the urban restriction area, and distance between the source intersection and central business district) affect noncompliance frequency. Results show that under the two policies, holding a nonlocal license plate, traveling a long distance daily in the urban restriction area, and departing from a location far from the city center increase the probability of noncompliance behavior of individuals. However, unrestricted travel rate, which represents the frequency of vehicle trips on unrestricted days, exhibits different influences on noncompliance behavior under the two policies. This study provides an in-depth interpretation to noncompliance behavior under vehicle restriction policies and aids improve the long-term effectiveness of the policies. [ABSTRACT FROM AUTHOR]

• Traffic congestion is resolved using a feeder bus. • The feeder bus connects residential areas and major transit points. • Road2vec, which uses word2vec to vectorize each road passed by a taxi, is proposed. • A feeder bus route based on the taxi movement pattern is designed. • The route considers transportation demand and traffic conditions at a specific time. Despite the existence of vast bus and subway networks, the demand for taxis in the morning commute hours is substantial in metropolitan areas. This kind of morning traffic can be resolved by means of feeder buses connecting residential areas with popular transit points. To assist the design of feeder bus routes, this study proposes an optimization approach based on road2vec, which is applied to real-time taxi GPS data. Road2vec is a neural network-based embedding methodology that extracts road name vectors considering the movement patterns of vehicles. Subsequently, the k-means clustering analysis is applied to those vectors to identify the major taxi transit clusters during the commute hours. For each cluster, we suggest a feeder bus route that can best reflect the taxi trajectory patterns. To find intermediate stops between the departure and origin of a feeder bus route, we solve an integer programming to maximize the cosine similarity between the origin road vector and the departure road vector subtracted by the road vectors of intermediate stops. The suggested routes based on our method differ from the existing routes in that they have a tendency to pass through residential areas, transit stations, and schools. In addition, the result suggests that the model developed in this study finds bus routes that could be suitable for feeder buses by accommodating areas where the demand for taxis is high in the morning. Our road2vec approach is expected to contribute to a reduction in traffic during rush hours. [ABSTRACT FROM AUTHOR]

There is considerable interest in the contribution that workplace-based mobility management interventions can make to achieving more sustainable mobility patterns. A number of studies have evaluated the impacts of workplace-based interventions on the commuting behaviour of staff, but the broader potential of such initiatives depends on the willingness of employers to support them. Little research has been carried out examining the perspectives of senior managers. The research which has been conducted has focused on employers located in urban or rural areas. This paper reports on in-depth interviews with senior managers of employers located in two peri-urban areas on the edge of the city of Bristol, south-west England. The research was carried out during a period when public funding was available to support the introduction of sustainable transport measures. The interviews aimed to find out whether senior managers perceived the promotion of sustainable transport as relevant to their business concerns, and how this varied between different types of organisation. The results showed that all managers believed that measures to increase the use of alternative modes for commuting and local business travel could be beneficial for their business, even if these benefits were indirect and difficult to quantify. The perceived benefits of sustainable transport included: helping to ease traffic congestion on the road network, thereby reducing associated delays and stress; helping employers manage excessive demand for car parking; improving staff wellbeing; and widening the recruitment opportunities among workers lacking access to a private car. Employers who perceived the greatest benefits were also the most willing to engage with public authorities in introducing new workplace-based mobility measures. The findings on employer support for mobility management are relevant not only to peri-urban areas but also to employment sites within other areas (in particular suburban areas and the rural hinterland) where the same challenges may apply of encouraging alternatives to single occupancy car use without the means to invest in comprehensive public transport. [ABSTRACT FROM AUTHOR]

Recurrent road traffic congestion in large cities is a blight that wastes a great deal of time and increases greenhouse gas emissions, fuel consumption, noise and stress. One way of combating such congestion is traffic control by means of traffic signals. The role of traffic signals is to temporally separate the different traffic flows at each intersection and alternately distribute the right of way with appropriate time durations. The real-time traffic control systems that have been developed during the three last decades include CRONOS-2 which has several advantages under congested situations, in particular, its capacity to control several intersections in a centralized way. A real-site trial has been performed on a zone consisting of five adjacent intersections. The results show significant benefits for total delay in the zone during congestion compared to the usual control strategy for this zone. These results vary for each intersection considered on its own. This trial has demonstrated the effectiveness of this system in complex and constrained traffic situations. [ABSTRACT FROM AUTHOR]

Abstract This paper examines the potential of the bicycle, as an access mode for transit trips, to reduce spatiotemporal inequalities in job accessibility in the megacity of São Paulo, Brazil. Three temporally dynamic potential job accessibility models are developed, (i) a GTFS-based (General |Transit Feed Specification) walk-and-ride model, (ii) an integrated GTFS-based bike-and-ride model that incorporates topography constraints, the availability of dedicated cycling infrastructure, waiting times at intersections and car traffic levels, and (iii) a car model that accounts for congestion using TomTom speed profiles. Cluster analysis is then used to analyze the geographic distribution of the associated improvements. The results show that bike-and-ride has the potential to substantially increase job accessibility in the different areas of the city, but does not result in a more equal spatial distribution of job accessibility, as measured by Gini coefficients. Most of the improvements are centered in middle to high income areas with good accessibility. Peripheral areas, that tend to be the poorest and have the lowest accessibility by transit, improve the least. The inclusion of the bicycle is not enough to counteract all of the other forces causing low job accessibility in these areas. [ABSTRACT FROM AUTHOR]

Highlights • Vehicle restriction is usually unable to reach the expectation on volume reduction. • Vehicle restriction stimulates more "illegal" travels and higher travel intensities. • Travel speed in most of the road segments can be improved by vehicle restriction. • Better implementations of traffic management measures are discussed. Abstract To mitigate externalities of the urban transportation system, various travel demand management policies have been tested across the world in the past decades. Among them, vehicle restriction is considered the most straightforward way to reduce travel demands. The effects of this policy, however, are still inconclusive. Using the massive license plate recognition (LPR) data in Langfang, China, this study investigates the differences of two typical license-plate-number-based vehicle restriction strategies, i.e., One-Day-Per-Week (ODPW) and Odd-And-Even (OAE). This study compares ODPW and OAE from two perspectives: travel demand and traffic condition over the road network. In addition to the change of traffic volume, the analysis of travel demand also includes the investigations of "illegal" travel, which denotes the road use of restricted vehicles, and travel intensity, which represents the travel frequency of certain vehicles in a time period. The changes of traffic condition are assessed by travel speed. Findings confirm that traffic volume declines as ODPW shifts to OAE, but not as significantly as expected. This less-than-full adjustment can be explained by the increase of illegal travel and travel intensity. The policy shift from ODPW to OAE significantly improves the travel speed in most of the road segments, whether in peak hours or off-peak hours. The conclusions in this study provide in-depth insights into the influence of vehicle restriction policies and contribute to the effective design of travel demand management strategies. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an alternative empirical strategy to test whether airlines internalize airport congestion corresponding to their share of traffic at the airport. In particular, we construct a hypothesis from theoretical derivation that if airlines do internalize airport congestion the airfare prices would be positively correlated with the interactive term of the airline's passenger number at the origin airport and the congestion delay level of this airport. We test this hypothesis with the Airline Origin and Destination Survey (DB1B) database and the Airline On-Time Performance database published by the US Bureau of Transportation Statistics (BTS). We find that the hypothesis is supported by the data, suggesting that airlines' behaviour is in line with the internalization theory. We further implement subsample analysis and find that airline type also plays a role in this matter. In particular, while full service carriers internalize airport congestion, low cost carriers do not. [ABSTRACT FROM AUTHOR]

Abstract We study different mixes of private and public supply of roads in a network with bottleneck congestion and heterogeneous users. There are two parallel links for one origin and destination pair and two groups of travellers, where the group with the higher value of time also has higher schedule delay values. Previous scholars argued that as users become more heterogeneous, they benefit more from product differentiation, making private supply of roads more efficient. However, we find that local monopoly power might also increase if there is a 'separating equilibrium', which is an equilibrium where at least one group only uses one private road due to the different combinations of toll and congestion of the two roads. The private road can thus increase its toll without worrying about the competition from the other road for this group: it has a local monopoly over them. This lowers the efficiency of private supply. The problem is especially severe with flat tolls, which are constant over the peak. With fine tolls – which vary continuously over the day – there tends to be a pooling equilibrium – where both types use both roads – and competition remains intense. Flat tolling is also worse for users than fine tolling, as it has higher generalized prices. [ABSTRACT FROM AUTHOR]

Abstract Bus services can be seen as a way to reduce traffic congestion where they can encourage a mode shift from car. However, they can also generate negative effects on traffic flow due to stop-start operations at bus stops. This paper aims to assess the net impact of bus operations on traffic congestion in Melbourne. The methodology used to achieve this aim comprised of three main stages. First, a primary survey was conducted to determine the mode shift from bus to car when buses are unavailable. This figure was used to estimate the positive impact of buses on relieving congestion. Second, the negative impact of buses was investigated by considering the effect of bus stop operations on vehicle traffic flow using microsimulation. Finally, the net effect was estimated by contrasting congestion measures determined from a traditional four step model between two scenarios: ‘with bus’ and ‘without bus’. The results indicated that Melbourne’s bus network contributes to reduce the number of severely congested road links by approximately 10% and total delay on the road network by around 3%. The highest congestion relief impact was found in inner Melbourne with a 7% decrease in vehicle time travelled and total delay, and 16% decrease in the number of heavily congested road links. In inner areas, the level of congestion is relatively high so the mode shift from car to bus, even if not as high as middle and outer areas, have a significant effect on relieving traffic congestion. Areas for future research are suggested such as investigating the long-term effect of buses on traffic congestion. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide new insights into commuters’ mode choice behavior in a monocentric closed city with endogenous population distribution, where a congested highway and a crowded railway provide commuting services for residents on a linear urban corridor. We first explore the typical equilibrium mode-choice patterns with exogenous city boundary and population distribution, and then incorporate the residents’ mode choice into an urban spatial equilibrium model, in which the residents’ household consumption, the residential location choice and the property developers’ housing production are also explicitly modeled. Using comparative static analysis, we find that the urban corridor expands with the increase of railway fare if there is no congestion in the bimodal transportation system, but it’s not necessarily the case if highway congestion and transit crowding cannot be ignored. We provide numerical evidence to show that the urban corridor possibly shrinks with the increase of railway fare once congestion effects are considered. We also discuss the changes of urban form, utility level of residents and social welfare with different railway fare and subsidy policies. Numerical results show that the distance-based fare policy with low subsidy should be preferred because it can realize the Pareto-improved social welfare and utility level of residents. [ABSTRACT FROM AUTHOR]

Confronted with negative environmental impacts, rising fuel costs and increasing congestion, many cities are implementing sustainable mobility measures to improve the flow of passenger and goods. Examples of these measures are use of public transport, cycling, walking, energy efficient vehicles, biofuels. The challenge before transport decision makers is which one(s) to choose for implementation as often there is no or limited quantitative data available on the subject. Moreover, the context of each city, its geographic and transport conditions restrict the generalization of results obtained in experienced cities. In this paper, we are investigating application of ideal solution based multicriteria decision making (MCDM) techniques namely fuzzy TOPSIS, fuzzy VIKOR, and fuzzy GRA for sustainability evaluation of urban mobility projects. A real application for city of Luxembourg is provided. Three projects are evaluated namely implementation of a new tramway in the city center of Luxembourg, re-organization of existing bus lines in the city to perform optimized service, and implementation of electric vehicle car-sharing stations in the city. Sensitivity analysis is performed to determine the influence of input parameters on modeling results. The proposed work is one of the first few works to investigate application of ideal-solution based multicriteria decision making techniques for sustainability evaluation of urban mobility projects under uncertainty. Besides, the best alternative is selected using veto thereby overcoming the limitations of single MCDM methods. [ABSTRACT FROM AUTHOR]

With growing concerns about greenhouse gas emissions and traffic congestion, there is an emphasis on encouraging shifts to public transport, for both short and long distance travel. Major differences exist across countries in how successful these efforts are, and the United States is often used as the key example of a country with a strong resistance to shifting away from private car use. Even within the United States however, there is strong heterogeneity across regions and across different types of travellers. This paper seeks to add empirical evidence to understand the drivers of mode choice for intercity travel, using stated choice data from two major US intercity corridors: the Northeast Corridor (NEC) and the Cascade Corridor. We develop a hybrid choice model that allows for deterministic and random variations across travellers in their preferences, some of which can be linked to underlying attitudinal constructs. Our results highlight extensive heterogeneity and provide interesting insights into the drivers of behaviour, and the relationship between attitudes and actual choices. As an example, we see that for some groups, notably West Coast respondents, a stronger anti-car attitude is counter-acted by a reduced utility for non-car modes when making choices, possibly due quality of public transport provision. Similarly, for other groups, such as older and female travellers, a reduced concern for privacy, which would benefit public transport, is counter-acted by a stronger pro-car attitude. These findings highlight the complex way in which attitudes can influence choices and provide insights for targeted policy interventions. Through scenario testing, we also show how future modal split might change depending on how these patterns of heterogeneity evolve over time, noting that the way this might happen is of course unknown at present. [ABSTRACT FROM AUTHOR]

• Exploits excess capacity along concurrent tolled roadway facilities during maintenance. • Joint public agency and public–private partnership action. • Proposes multi-level, mixed-integer program for scheduling actions and setting tolls. • Determines fair compensation for concessionaires for reducing or suspending tolls. • Case study shows approach can lead to substantial savings in total social costs. This study considers the possibility of exploiting excess capacity along concurrent tolled roadway facilities operating in a public–private partnership for the purpose of alleviating traffic congestion during improvement action execution on a public roadway facility. Increased access to the tolled facility is enabled by reducing or suspending tolls during periods of roadway improvement action and appropriately compensating the tolled facility concessionaire for expected losses. The problem of determining an optimal reduced toll pricing strategy and compensation level is referred to as the Maintenance Timing and Price Discovery (MTPD) problem. A multi-level optimization model and iterative solution method are proposed to solve this problem. As toll prices often depend on traffic levels, the model also determines the optimal timing and time-of-day and day-of-week for executing the set of required improvement actions over the planning horizon to ensure a lowest total cost. The multi-level model consists of two bilevel models. Solution of the greater model is attained at a Stackelberg equilibrium between these two bilevel models. An additional Stackelberg equilibrium is sought between upper and lower levels of each of these two bilevel models individually. The MTPD formulation and general solution methodology are applied on an illustrative example from which computational results are obtained. Insights from the results show that this strategy for alleviating traffic congestion during roadway maintenance or other improvement activities can lead to substantial savings in total agency and user costs. [ABSTRACT FROM AUTHOR]

Road network congestion; a traffic state characterised by slower speeds, longer trip times, and increased vehicular queuing; is a major issue in most urban areas around the globe. Building more roads is a commonly employed policy intervention to reduce congestion. This strategy, however, is controversial because under certain conditions road capacity expansions may induce growth in traffic volumes. A crucial precursor to understanding whether road capacity expansions provide a solution to congestion is to quantify the technology driving congestion in urban road networks. This congestion technology describes the variation in performance of the network, often represented by traffic flow through the road network, over its intensity of use given by the number of vehicles in the network. However, obtaining empirical estimates of congestion technology from data on traffic variables is challenging due to statistical biases that emerge via the complex interactions between traffic flow, traffic controls, and capacity. To adjust for such biases, this paper presents an approach based on causal statistical modelling to quantify the nature and form of congestion technology in road networks in twenty-four cities worldwide. Our results suggest that increasing network capacity is in general not an efficient solution to manage congestion, in the sense that the average travel speed in the network does not increase substantially with an increase in capacity. This result and our congestion technology estimates have important implications for optimal urban transportation strategies. [ABSTRACT FROM AUTHOR]

In this research, we investigate the acceptability of three new and emerging smart mobility options and quantify the associated willingness-to-pay values in the context of Lisbon using a comprehensive stated preferences (SP) survey. The smart mobility options include shared taxi, one-way car rental, and a novel combination of park-and-ride and school bus facilities. While previous surveys on smart mobility options had investigated limited number of alternatives in isolation, the SP survey used in this research presents the smart mobility options alongside the existing options and their traditional variants like congestion pricing and improved public transport systems. Further, the choice of mode, departure time and occupancy are investigated in a multidimensional framework. This resulted a large choice set (with 9 modes, 5 departure times, and 2 occupancy levels leading to 135 alternatives in total) and required a novel survey design. The main survey administered over the internet and computer aided personal interviews included 2372 valid SP observations from 1248 respondents. Multi-dimensional mixed logit models were used to capture the complex correlations introduced due to the non-traditional survey design. Results indicate a significant preference of one-way car rental and shared taxi for non-commute trips. For commute trips, improved versions of traditional public transport modes are favoured over smart mobility options. These findings, as well as the novel data collection and modelling approach, are expected to provide important information to transportation planners and policy makers working to implement smart mobility options in Lisbon as well as in other cities. [ABSTRACT FROM AUTHOR]

Serious congestion problems at slot-controlled airports worldwide call for some action. Slot scheduling related research has mainly focused on scheduling models allocating airport capacity by optimising scheduling efficiency. However, existing literature does not capture the effect of slot allocation decisions on the acceptability of slot schedules. The objective of this paper is to investigate the trade-off between scheduling efficiency and the airlines’ dis-utility of slot schedules expressed by various metrics of schedule displacement. We develop and solve two bi-objective scheduling models considering different combinations of total and maximum acceptable slot displacement objectives. The proposed models are applied to real-world scheduling data. Substantial improvements in schedule acceptability metrics are achieved without sacrificing a lot in terms of scheduling efficiency. Sacrifices in scheduling efficiency increase the capability of the airport coordinator to allocate slots that are eventually acceptable and hence more intensively used. [ABSTRACT FROM AUTHOR]

This paper explores the implications of an imperfectly competitive non-aeronautical market for cost recovery at a congestible airport. We show that aeronautical revenue under welfare maximization is short of its costs, because the optimal airport charge must be set lower than the first-best level, to counteract market power in the non-aeronautical market, which results in insufficient revenue. However, imperfectly competitive non-aeronautical service (“side business”) leads to a positive profit that the airport may share, thereby improving the chance to recover costs. We demonstrate that airport overall cost recovery, including the profit from side business, may be satisfied, depending on the profit from side business and the demand elasticity of side business. Our analysis further identifies the role of “another good,” which can be a substitute or a complement for non-aeronautical service, in cost recovery. We analyze both cases of exogenous and endogenous prices of another good. When the price of another good is exogenous, the price higher (lower) than the marginal cost in the another-good market makes cost recovery less (more) likely. When the price of another good is endogenous, cost recovery is more likely (unlikely) to hold when non-aeronautical service and another good are closer complements (substitutes) or/and the airport absorbs more (less) profits from side business. The possibility that a monopoly airport fails to satisfy cost recovery is also examined. [ABSTRACT FROM AUTHOR]

This paper synthesizes the major interventions available to manage airport demand and capacity, the analytical tools that may support the underlying policy, managerial and operational decisions, and guidelines for policy and practice obtained from recent research. The resulting insights fall into three broad categories. First, airport throughput exhibits significant variability, and airport capacity depends on the available infrastructure and operating procedures. Second, airport on-time performance is highly non-linear, and thus sensitive to variations in demand and capacity. Third, airport demand management involves a trade-off between mitigating congestion and maximizing capacity utilization, and scheduling mechanisms can support and enhance existing practices. The implications for the development and management of airport systems worldwide are discussed. [ABSTRACT FROM AUTHOR]