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• 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]

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]

• 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]

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]

• 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]

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]

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]

Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies have claimed that ridesourcing also brings negative externalities (e.g., inducing trips and aggravating traffic congestion). Therefore, many cities are planning to enact or have already enacted policies to regulate its use. However, these policies' effectiveness or impact on ridesourcing demand and traffic congestion is uncertain. To this end, this study applies difference-in-differences (i.e., a regression-based causal inference approach) to empirically evaluate the effects of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago. It shows that this congestion tax policy significantly curtails overall ridesourcing demand but marginally alleviates traffic congestion. The results are robust to the choice of time windows and data sets, additional control variables, alternative model specifications, alternative control groups, and alternative modeling approaches (i.e., regression discontinuity in time). Moreover, considerable heterogeneity exists. For example, the policy notably reduces ridesourcing demand with short travel distances, but such an impact is gradually attenuated as the distance increases. [ABSTRACT FROM AUTHOR]

Congestion pricing is a standard approach to mitigate traffic congestion in a number of urban networks around the world. The advancement of satellite technology has spurred interest in distance-based congestion pricing schemes, which obviate the need for fixed infrastructure such as gantries that are used in area- and cordon-based pricing. Moreover, distance-based pricing has the potential to more effectively manage traffic congestion. In the context of distance-based congestion pricing, we propose the use of sparse subspace clustering methods employing Elastic Net optimization (SSCEL) and Orthogonal Matching Pursuit (SSCOMP), as well as two hierarchical density-based clustering methods, (OPTICS, HDBSCAN*) for the derivation of tolling zones. These tolling zones are then used within a simulation-based framework for real-time predictive distance-based toll optimization to examine network congestion and performance of the tolling schemes. Within this framework, for a given definition of tolling zones, tolling function parameters are optimized in real-time using a simulation-based Dynamic Traffic Assignment (DTA) model. Guidance information generation is integrated into the predictive optimization framework and behavioral responses to the information and tolls along dimensions of departure time, route, mode, and trip cancellation are explicitly modeled. For the evaluation of network performance we make use of Travel Speed Index (TSI) data from the real-world Boston Central Business District urban network and demonstrate that tolling zones derived from the sparse subspace clustering are an effective means of operationalizing real-time distance-based toll optimization schemes, showing improvements in average travel time and social welfare relative to the baseline. [ABSTRACT FROM AUTHOR]

With the government planning a congestion charging (CC) scheme for Jakarta, public support is regarded as a prerequisite for its implementation. Politicians typically see CC reform as a controversial policy if there is no public support. Yet a CC plan is currently under active development and remains under consideration as a way to mitigate the acute traffic congestion in Jakarta. The CC theme has been recognized as a powerful instrument in both delivering efficient road use and raising revenues. Studies indicate that revenue redistribution is one of the most important determinants of public support for such scheme. Given the absence of studies from the Asian megacity context, this paper presents a systematic study of how public perceptions relate to revenue spending choice behavior. A stated preference (SP) questionnaire is developed to investigate citizen’s perceptions of CC reform, their preferences for revenue redistribution and their current travel behavior. Using data collected using this SP questionnaire in 2013, a hybrid revenue allocation (HRA) model is formulated that captures the impact of tangible factors (i.e. charges, socio-demographic characteristics, travel behavior) and intangible factors (perceptions) during process of deciding among revenue spending alternatives. The proposed model is drawn from traditional utilitarian resource (time) allocation theory, with an extension consisting of latent constructs representing subjective individual psychological perceptions. We find that there is a strong correlation between revenue allocation preferences and an individual’s subjective psychological perceptions. The most preferred revenue allocations are for public transport improvements and traffic safety improvements, with 54% and 29% agreement, respectively. We find there is little support for spending revenues on other issues such as for stimulating local business (13%) and for parks/green spaces, driving education/enforcement and improving the parking system (4% in total). The findings of sensitivity analysis further disclose that the choice of spending on public transport improvements among respondents who frequently visit the central business district (CBD) is sensitive to the level of the CC charge, while for respondents who frequently enter the CBD by car and have a medium-high income the traffic safety allocation is sensitive to charge. [ABSTRACT FROM AUTHOR]

Efficient parking management strategies are vital in central business districts of cities where parking is limited and congestion is intense. Hourly parking pricing is a common parking management strategy where vehicles pay based on their parking duration (dwell time). In this paper, we derive comparative static effects for a small network to show that road pricing and hourly parking pricing are structurally different in how they influence the traffic equilibrium with elastic demand. Whereas road pricing strictly reduces demand, hourly parking pricing can reduce or induce demand depending on the parking dwell time elasticity (to the hourly parking price). When dwell time is elastic, demand always increases with parking price. However, when dwell time is inelastic, demand may increase or decrease with the parking price. Hence, hourly parking pricing can actually cause higher congestion and decay social welfare if imposed imprudently. For larger networks, we present a Variational Inequality model that characterizes the emergent equilibrium. Numerical experiments on a large network validate our analytical findings from a smaller and stylized case study. Our results also show a lower standard deviation in the parking search time (i.e., time to find a parking spot) when dwell time is highly elastic to the hourly parking price. [ABSTRACT FROM AUTHOR]

This paper proposes a novel concept of congestion pricing based on voluntary peer-to-peer exchange of money between motorists in exchange for one ceding priority to another in a traffic stream. While in the classical congestion charging paradigm payments are compulsory and flow only towards the system operator, in the proposed marketplace participation is voluntary and motorists directly compensate each other. A particular motorist may find that he/she is a ‘payer’ at certain points in a given journey and a ‘payee’ at others. Humans would not be expected to successfully seek, negotiate and execute a continuous series of peer-to-peer trades involving micro-payments while also handling the cognitively-demanding task of driving; real-world implementation will therefore require vehicles operating under fully-automated control in both the longitudinal and lateral dimensions during the time periods that they seek and engage in trades. The automated vehicle control algorithms must be sufficiently intelligent and adaptable to enable alternative maneuvers on short timescales, given the inherent uncertainty of whether or not a potential trade will in fact be executed. The peer-to-peer trading would be executed algorithmically, subject to strategic-level guidance given by a vehicle’s occupant(s) regarding the occupant’s relative valuation of money and priority in the traffic stream. In this paper we detail the prospective marketplace and present a simple simulation model to expose its properties. We show that the proposed peer-to-peer marketplace could lead to both desirable and undesirable outcomes; which of these would be predominant is a matter requiring empirical study. The paper concludes with a discussion of further research needs to refine and develop these concepts into practice. [ABSTRACT FROM AUTHOR]

This paper estimates the traffic volume and travel time effects of the road congestion pricing implemented on the San Francisco-Oakland Bay Bridge. I employ both difference-in-differences and regression discontinuity approaches to analyze previously unexploited data for the two years spanning the price change and obtain causal estimates of the hourly average treatment effects of the policy. I find evidence of peak spreading in traffic volume and decreases in travel time during peak hours. I also find suggestive evidence of substitution to a nearby bridge and decreases in travel time variability. In addition, I calculate own- and cross-price elasticities. [ABSTRACT FROM AUTHOR]

Road pricing may provide a solution to increasing traffic congestion in metropolitan areas. Route, departure time and travel mode choices depend on risk attitudes as commuters perceive the options as having uncertain effects on travel times. We propose that Experimental Economics methods can deliver data that uses real consequences and where the context can be varied by the researcher. The approach relies on the controlled manipulation of contexts, similar to what is done in the Stated Choice approach, but builds in actual consequences, similar to the Revealed Preference approach. This paper investigates some of the trade-offs between the cost of conducting Experimental Economics studies and the behavioral responses elicited. In particular, we compare responses to traditional lottery choice tasks to the route choice tasks in simulated driving environments. We also compare students (a low cost convenient participant pool) to field participants recruited from the driving population. While we see initial differences across our treatment groups, we find that their risk taking behavior converge with minimal repetition. [ABSTRACT FROM AUTHOR]

Given the shift toward energy efficient vehicles (EEVs) in recent years, it is important that the effects of this transition are properly examined. This paper investigates some of these effects by analyzing annual kilometers traveled (AKT) of private vehicle owners in Stockholm in 2008. The difference in emissions associated with EEV adoption is estimated, along with the effect of a congestion-pricing exemption for EEVs on vehicle usage. Propensity score matching is used to compare AKT rates of different vehicle owner groups based on the treatments of: EEV ownership and commuting across the cordon, controlling for confounding factors such as demographics. Through this procedure, rebound effects are identified, with some EEV owners found to have driven up to 12.2% further than non-EEV owners. Although some of these differences could be attributed to the congestion-pricing exemption, the results were not statistically significant. Overall, taking into account lifecycle emissions of each fuel type, average EEV emissions were 50.5% less than average non-EEV emissions, with this reduction in emissions offset by 2.0% due to rebound effects. Although it is important for policy-makers to consider the potential for unexpected negative effects in similar transitions, the overall benefit of greatly reduced emissions appears to outweigh any rebound effects present in this case study. [ABSTRACT FROM AUTHOR]

Estimation of congestion costs, presumed to be one of the largest external costs of automobile travel, is typically based on a single value of time delay for motorists in metropolitan areas. However, the estimation may be wrong if the profiles of motorists are different at different times of day. This study uses a survival model to examine the demographic and socioeconomic profiles of motorists at different times of day at congested locations in southern California, by using on-road remote-sensing measurements and license plates images obtained in 2007 and 2008 by the California South Coast Air Quality Management District (SCAQMD). More than 80,000 vehicles were observed from fifteen selected study sites over fifteen days. Their plates, through anonymized registration records, revealed addresses at the census block group level, which have homogenous profiles by construction. Motorists’ profiles at different times of day display large variation, however, according to extended Cox model with a non-parametric baseline hazard, which is used to accommodate both the time-invariant and time-varying effects of the covariates. This study thus proposes a new approach to examine heterogeneity among motorists. [ABSTRACT FROM AUTHOR]

This study estimates the effects of an advanced traveler general information system (ATGIS), which includes fuel consumption and health-related emissions cost information on transportation network users’ travel choice behavior for recurrent congestion conditions. The effects are estimated using four different formulations based on four different behavioral assumptions. Incorporating stochastic features in link cost estimation rather than in route choice, we provide a novel modeling approach that enables us to use transportation planning models of major metropolitan areas without a need for major computationally-expensive changes in the existing models. We examined the effects of an ATGIS on the Fresno, CA, road network and found several interesting results. First, the ATGIS impact is closely related to pre-system (prior to the implementation of an ATGIS) perceived fuel and emissions costs. Total travel time in the city can be reduced by 17% (no pre-system perceived costs) to 1% (accurate pre-system perceived costs), and even increased by 1% (higher-than-actual pre-system perceived costs). Second, the addition of emissions costs, although negligible relative to fuel and time costs, can effectively reduce total system-wide travel time by up to 1% and fuel consumption by up to 0.6% during peak hours. Third, the ATGIS can reduce annual social costs by as much as $1053 million (high gas price, no pre-system perception) to $48 million (medium gas price, accurate pre-system perception), which are comparable to social cost savings by a congestion pricing (CP) scheme in the study area. [ABSTRACT FROM AUTHOR]

Highlights: [•] The exploration of the dynamic impacts of area based congestion pricing. [•] The modeling individual travel mode perceptions by building on fuzzy set representations. [•] The relationship between travel congestion mitigation and mode improvements. [•] The long-term effects of an area based congestion pricing. [•] Policy analysis through a management flight simulator of an area-based congestion pricing scheme. [ABSTRACT FROM AUTHOR]

Abstract: In determining the marginal cost of congestion, economists have traditionally relied upon directly measuring traffic congestion on network links, disregarding any “network effects,” since the latter are difficult to estimate. While for simple networks the comparison of the network-based congestion costs with the link-based ones can be done within a theoretical framework, it is important to know whether such network effects in real large-scale networks are quantitatively significant. In this paper we use a strategic transportation planning model (START) to compare marginal congestion costs computed link-by-link with measures taking into account network effects. We find that while in aggregate network effects are not significant, congestion measured on a single link is a poor predictor of total congestion costs imposed by travel on that link. Also, we analyze the congestion proliferation effect on the network to see how congestion is distributed within an urban area. [Copyright &y& Elsevier]

This paper presents an empirical application of a congestion–alleviation strategy that C. Daganzo [Transportation Research B 29 (1995) 139–154] proposed as a “hybrid between rationing and pricing”. This strategy is applied to the San Francisco Bay Bridge corridor, in search of a practical and Pareto-improving solution to the Bridge''s congestion. The work relies on a mode-split model for work trips across four different income groups residing in 459 origin zones, and it applies an equilibrium analysis based on Bridge performance. Results indicate that modal utilities (and thus choices) are sensitive to the specific combination of toll and rationing rate, as well as to the Bridge''s travel-time (or performance) function, and the length of the congested section. Though no combination of tolls plus rationing rates was found to benefit all groups of travelers studied, further investigations may improve upon these results by refining some of the assumptions made here. [Copyright &y& Elsevier]