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Traffic congestion has been considered one of the most serious global issues confronting all nations in recent years. Basrah City’s highways in general, and particularly the highways surrounding and leading to the University of Basrah, the campus of Bab Azzubair, and the college complex, have experienced traffic congestion and higher delays. The focus of this study is to propose some solutions to mitigate traffic congestion and higher delays on these highways. Field traffic data and questionnaires where SPSS was used to analyse the collected data, and Highway Capacity Software (HCS 2010) was used to assess the level of service (LOS) on the highways. The results showed that the LOS reaches level F on Baghdad Street, while other streets range from level B to level C. Two proposals have been suggested to improve the LOS on Baghdad Street: (1) applying a park-and-ride system; and (2) widening Baghdad Street by adding two lanes in each direction. The LOS for both proposals has been evaluated, and the results showed that the first proposal performed better. The analysis of the collected data also showed that public transportation services are very limited on the highways surrounding and leading to the Bab Azzubair campus. Therefore, public transportation services should be promoted and enhanced in Basrah City. [ABSTRACT FROM AUTHOR]

Using data from the 545 largest European cities, we study whether the expansion of their highway capacity provides a solution to the problem of traffic congestion. Our results confirm that in the long run, and in line with the 'fundamental law of highway congestion', the expansion in cities of lane kilometres causes an increase in vehicle traffic that does not solve urban congestion. We disentangle the increase in traffic due to the increases in coverage and in capacity. We further introduce road pricing and public transit policies in order to test whether they moderate congestion. Our findings confirm that the induced demand is considerably smaller in cities with road pricing schemes, and that congestion decreases with the expansion of public transportation. [ABSTRACT FROM AUTHOR]

Traffic congestion is the major problem due to rapid urbanization and exponential growth of private vehicles, a decrease in the use of active transport, which results in problems of transport sustainability and public health issues. Generally, commuters often find it challenging to travel along the congested routes in urban areas due to increased travel time or air pollution. The traffic congestion of those congested routes may be minimized using strategies like congestion pricing, but public acceptability is one of the main hurdles in establishing a congestion pricing scheme. The present study attempted to understand the public acceptability of the congestion pricing system in India. To fulfil the objective of the study, a questionnaire survey was conducted to ascertain the public's perception of the congestion pricing system. In order to achieve this objective, a multinomial logistic regression (MNL) model was developed by considering public opinion on support to implement congestion pricing as a dependent variable, and other variables were considered as independent variables. From the model results, it is understood that the travel frequency (viz., 1-2 and 3-4 times) and number of times struck in congestion of commuters are most likely to remain neutral. The revenue generated by congestion pricing is allocated to road infrastructure, car tax reductions, public transportation, and parking areas, which are more likely to remain in neutral and under review after implementation. The study results are more useful to policy makers in urban areas while they relook at congestion pricing strategies in developing countries. [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]

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities. [ABSTRACT FROM AUTHOR]

This paper is designed to evaluate and improve the effectiveness of transportation systems and reduce traffic congestion through the use of simulation models and scenario development. A system dynamics framework is used to test and evaluate the alternatives of future strategies for the city of Surabaya, Indonesia. Some factors affecting the effectiveness of transport systems include operational effectiveness and service effectiveness, as well as uncertainty. To improve the effectiveness of transportation systems, several strategies can be implemented, such as subsidizing public transportation, increasing the cost of private vehicle parking fees, raising taxes on private vehicles, and reducing delays in public transportation through scenario development. Scenario results show that, by pursuing these strategies, effectiveness could be improved by 80% as the impact of the increase in operational and service effectiveness, helping to mitigate traffic congestion. Congestion could be reduced to 70% (on average) due to the decrease in daily traffic. [ABSTRACT FROM AUTHOR]

With the rapid development of society, urban traffic congestion has gradually become an important social problem that many cities need to solve. For Chongqing, traffic congestion not only affects residents' normal travel but also brings more serious environmental pollution. Aiming at the problem of urban traffic congestion and automobile exhaust pollution, this paper adopts the system dynamics method to establish a model for studying urban traffic congestion system from the perspectives of private cars, trucks, and public transportation. First, we determine city motor vehicle trips as an indicator of the degree of traffic congestion in this paper. Second, we analyze the causal relationship between the growth of private cars, the travel of trucks, public transportation, population, and other factors and then build a model and test the stability of the model. Then, we add some practical policies to the model for policy analysis. Finally, it is concluded that the private car restriction policy is effective in controlling the amount of private car travel, and the purchase restriction policy controls the growth of the number of private cars from the root cause, but the development of public transportation is the most effective treatment measure in the long run. [ABSTRACT FROM AUTHOR]

Transportation agencies face escalating challenges in forecasting the traffic demand. Traditional prediction methods focused on individual transportation sectors and failed to study the inter-dependencies between the different transportation systems. Hence, there is a need for more advanced and holistic modeling techniques. To this end, this paper models and analyses an urban transportation system-of-systems incorporating seven various systems: population and GDP, CO2 emission, gasoline price and total vehicle trips, traffic demand, public and private transportation, transportation investment, and traffic congestion. Accordingly, this research simulates transportation networks as a collection of task-oriented systems that combine their resources to form a complex system with increased functionality. The goal of this paper is to understand the traffic complex behavior of urban transportation networks and to study the interdependencies between the different variables. The proposed framework could be implemented to any urban city, county, state, or country. The developed model incorporates a hybrid modeling approach that includes: logistic model, system dynamics, stochastic cellular automata, chaos theory, and Lotka-Volterra model. The final model is demonstrated using a case study. The contribution of this paper lies in modeling the transportation network as a dynamic system of systems rather than as static model as provided in previous studies. [ABSTRACT FROM AUTHOR]

Urban public transport is a very essential mode for urban residents' commute travel; however, the unbalanced spatial and temporal distribution of travel demand usually leads to passenger flow congestion risk at certain section and time. Meanwhile, the risk is short of quantified description. Based on the Pressure-State-Response framework, the study puts forward three bus passenger flow congestion risk evaluation indexes including the alternative pressure, the congestion intensity, and the transport efficiency. Then, the evaluation model is proposed based on the entropy method, and the risk is divided into four levels by K -means clustering. The article considers the 3rd Ring Road corridor in Beijing as a case to identify the risk level. The results show that the risk in the peak hours of weekdays is generally about 1.5 times higher than the risk in the weekends. The congestion risk is stable in level 3 during the majority time of morning peak hours. The duration intensity of level 4 risk is less than 0.1 during weekdays, indicating that the highest flow congestion can be quickly evacuated in a short time. The integrated passenger risk identification and evaluation model was proposed to identify the passenger flow risk level and induce the network flow distribution more reasonable. The study also provides technical support for ensuring the public transit system safety. [ABSTRACT FROM AUTHOR]

Understanding congestion in space-time domain using performance measures is essential prior to suggesting improvement schemes to reduce congestion. With technological advances like Global Positioning System (GPS), many metropolitan planning organizations give more emphasis on travel time based performance measures to quantify congestion, than on traditional way of using volume-to-capacity (V/C) ratios. In India, often it may not be possible to use personal vehicles as probes for travel time data collection. However, the public transit buses fitted with GPS devices could be used as cheap and effective probes to estimate the congestion status of other types of vehicles in the stream. The present study is an attempt in this direction. Two bus transit routes in Chennai, India were considered as case studies in order to cover the wide range of geometric and traffic conditions on urban arterials. GPS-fitted buses on these routes were used as probes in congestion quantification. As the dwell time at bus stops is a unique characteristic of transit buses when compared to other vehicles in the stream, a methodology has been proposed to find the dwell times including acceleration and deceleration times based on the approaching and departing speeds at bus stops. Regression models were then developed to predict the Congestion Index (CI) for various types of vehicles using bus CI, weighted carriageway width and the presence or absence of signalized intersection as independent variables. The results are promising and could be considered for real-time display of congestion levels for Advanced Traveler Information System (ATIS) applications. [ABSTRACT FROM AUTHOR]

This paper examines the design of the credit-based congestion management schemes that achieve Pareto-improving outcome in general two-mode networks. It is assumed that transit is a slower but cheaper alternative to driving alone. The distributional welfare effects of congestion pricing on users with the different value of time (VOT) in Liu and Nie (Trans Res Board 2283:34-43, 2012) are used in developing Pareto-improving credit schemes. We show that, similar to the single-mode model, the sufficient and necessary condition for the existence of a discriminatory Pareto-improving credit scheme is the reduction in the total system cost. A sufficient condition for the existence of an anonymous Pareto-improving credit scheme is also derived. A cross-OD subsidization scheme is proposed when the sufficient condition is not satisfied for each origin-destination (O-D) pair. Numerical experiments on the expanded Sioux Falls networks with a log-normal VOT distribution demonstrate that the proposed Pareto-improving scheme can generate positive net revenue in the presence of good transit coverage. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization model to minimize the 'system costs' and guide travelers' behavior by exploring the optimal bus investment and tradable credits scheme design in a bimodal transportation system. Travelers' transport mode choice behavior (car or bus) and the modal equilibrium conditions between these two forms of transport are studied in the tradable credits scheme. Public transport priority is highlighted by charging car travelers credits only. The economies of scale presented by the transit system under the tradable credit scheme are analyzed by comparing the marginal cost and average cost. Numerical examples are presented to demonstrate the model. Furthermore, the effects of tradable credits schemes on bus investment and travelers' modal choice behavior are explored based on scenario discussions. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Lack of public and political acceptance is the main barrier to introducing congestion charges. Here we compared the experiences of congestion charge introduction in the Swedish cities of Stockholm and Gothenburg, with the aim of explaining differences in political and public acceptance. The results showed the importance of procedural factors, such as the consistency of objectives in policy packages, communication and marketing efforts, and the use of public referendums, and of contextual factors, including urban form, level of congestion, and functioning of public transport. Important lessons were drawn between the two cities, but primarily on how to design, rather than secure public acceptance for, a congestion tax scheme. To build acceptance for congestion charges, close attention must be paid to the local political and geographical context when designing and implementing such a scheme. [ABSTRACT FROM AUTHOR]

The London congestion charging scheme was introduced in 2003 alongside a series of other changes to the transport system, most notably improved bus services. Research on the effects of the scheme usually attributes (often implicitly) all the changes that took place in central London since 2003 to the congestion charging, while the effects of other factors, as well as the effects of trends over time, are not usually (explicitly) considered. This paper revisits the results of the London scheme to examine to what degree observed effects (related to congestion and traffic levels, changes in travel behaviour and air pollution) can be fully or partly attributed to congestion charging. Attention is also given to the amount of revenues the scheme generated. While there is no dispute over the theoretical rationale for introducing congestion charging, the London scheme reveals that questions can be raised with regard to its practical effectiveness. [ABSTRACT FROM PUBLISHER]

Abstract: This paper investigates the impact of cordon-based congestion pricing scheme on the mode-split of a bimodal transportation network with auto and rail travel modes. For any given toll-charge pattern, its impact on the mode-split can be estimated by solving a combined mode-split and traffic-assignment problem. Using a binary logit model for the mode-split, the combined problem is converted into a traffic-assignment problem with elastic demand. Probit-based stochastic user equilibrium (SUE) principle is adopted for this traffic-assignment problem, and a continuously distributed value of time (VOT) is assumed to convert the toll charges and transit fares into time-units. This combined mode-split and traffic-assignment problem is then formulated as a fixed-point model, which can be solved by a convergent Cost Averaging method. The combined mode-split and traffic-assignment problem is then used to analyze a multimodal toll design problem for cordon-based congestion pricing scheme, with the aim of increasing the mode-share of public transport system to a targeted level. Taking the fixed-point model as a constraint, the multimodal toll design problem is thus formulated as a mathematical programming with equilibrium constraints (MPEC) model. A genetic algorithm (GA) is employed to solve this MPEC model, which is then numerical validated by a network example. [Copyright &y& Elsevier]

• This study models the ridesharing program in a doubly dynamical system. • This study adopts a trip-based MFD approach to model within-day traffic dynamics. • This study finds that the emerging ridesharing program may not necessarily reduce traffic congestion. • This study develops and compares different pricing strategies to improve system performance. This study models a multi-modal network with ridesharing services. The developed model reproduces the scenario where travelers with their own cars may choose to be a solo-driver, a ridesharing driver, a ridesharing rider, or a public transit passenger while travelers without their own cars can only choose to be either a ridesharing rider or a public transit passenger. The developed model can capture the (clock) time-dependent choices of travelers and the evolution of traffic conditions, i.e., the within-day traffic dynamics. In particular, the within-day traffic dynamics in a city region is modeled through an aggregate traffic representation, i.e., the Macroscopic Fundamental Diagram (MFD). This paper further develops a doubly dynamical system that examines how the within-day time-dependent travelers' choices and traffic conditions will evolve from day to day, i.e., the day-to-day dynamics. Based on the doubly dynamical framework, this paper proposes two different congestion pricing schemes that aim to reduce network congestion and improve traffic efficiency. One scheme is to price all vehicles including both solo-driving and ridesharing vehicles (for ridesharing trips, the price is shared by the driver and rider), while the other scheme prices the solo-driving vehicles only in order to encourage ridesharing. The pricing levels (under each scheme) can be determined either through an adaptive adjustment mechanism from period to period driven by observed traffic conditions, or through solving a bi-level optimization problem. Numerical studies are conducted to illustrate the models and effectiveness of the pricing schemes. The results indicate that the emerging ridesharing platform may not necessarily reduce traffic congestion, but the proposed congestion pricing schemes can effectively reduce congestion and improve system performance. While pricing solo-driving vehicles only may encourage ridesharing, it can be less effective in reducing the overall congestion when compared to pricing both solo-driving and ridesharing vehicles. [ABSTRACT FROM AUTHOR]