Optimization-based study of the location of park-and-ride facilities

Park-and-ride facilities are parking lots located on the periphery of cities to intercept car trips coming from the suburbs and diverting them to a transit system, thus playing a potentially important role in reducing traffic congestion and air pollution in urban areas. In this paper, we present a study carried out to shed light on where to install park-and-ride facilities in the city of Coimbra, central Portugal. Its main component is an optimization model which aims to determine the best possible locations for a given number of park-and-ride facilities under the objective of minimizing car use in the urban areas. The main result of the study is that the introduction of only three park-and-ride facilities could reduce car use in Coimbra's urban areas by 19%, an impact that would be very difficult to achieve through measures such as decreasing bus fares, increasing parking fees and/or increasing bus services.     

A dynamic cordon pricing scheme combining the Macroscopic Fundamental Diagram and an agent-based traffic model

Pricing is considered an effective management policy to reduce traffic congestion in transportation networks. In this paper we combine a macroscopic model of traffic congestion in urban networks with an agent-based simulator to study congestion pricing schemes. The macroscopic model, which has been tested with real data in previous studies, represents an accurate and robust approach to model the dynamics of congestion. The agent-based simulator can reproduce the complexity of travel behavior in terms of travelers’ choices and heterogeneity. This integrated approach is superior to traditional pricing schemes. On one hand, traffic simulators (including car-following, lane-changing and route choice models) consider travel behavior, i.e. departure time choice, inelastic to the level of congestion. On the other hand, most congestion pricing models utilize supply models insensitive to demand fluctuations and non-stationary conditions. This is not consistent with the physics of traffic and the dynamics of congestion. Furthermore, works that integrate the above features in pricing models are assuming deterministic and homogeneous population characteristics. In this paper, we first demonstrate by case studies in Zurich urban road network, that the output of a agent-based simulator is consistent with the physics of traffic flow dynamics, as defined by a Macroscopic Fundamental Diagram (MFD). We then develop and apply a dynamic cordon-based congestion pricing scheme, in which tolls are controlled by an MFD. And we investigate the effectiveness of the proposed pricing scheme. Results show that by applying such a congestion pricing, (i) the savings of travel time at both aggregated and disaggregated level outweigh the costs of tolling, (ii) the congestion inside the cordon area is eased while no extra congestion is generated in the neighbor area outside the cordon, (iii) tolling has stronger impact on leisure-related activities than on work-related activities, as fewer agents who perform work-related activities changed their time plans. Future work can apply the same methodology to other network-based pricing schemes, such as area-based or distance-traveled-based pricing. Equity issues can be investigated more carefully, if provided with data such as income of agents. Value-of-time-dependent pricing schemes then can also be determined.     

Measuring marginal congestion costs of urban transportation: Do networks matter?

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.     

Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing

Cruising-for-parking constraints mobility in urban networks. Car-users may have to cruise for on-street parking before reaching their destinations. The accessibility and the cost of parking significantly influence people's travel behavior (such as mode choice, or parking facility choice between on-street and garage). The cruising flow causes delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore important to understand the impact of parking limitation on mobility, and to identify efficient parking policies for travel cost reduction. Most existing studies on parking fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general, lack the treatment of dynamics of the cruising-for-parking phenomenon, or require detailed input data that are typically costly and difficult to collect. In this paper, we propose an aggregated and dynamic approach for modeling multimodal traffic with the treatment on parking, and utilize the approach to design dynamic parking pricing strategies. The proposed approach is based on the Macroscopic Fundamental Diagram (MFD), which can capture congestion dynamics at network-level for single-mode and bi-modal (car and bus) systems. A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion. Pricing strategies are developed with the objective of reducing congestion, as well as lowering the total travel cost of all users. A case study is carried out for a bi-modal city network with a congested downtown region. An elegant feedback dynamic parking pricing strategy can effectively reduce travel delay of cruising and the generic congestion. Remarkably, such strategy, which is applicable in real-time management with limited available data, is fairly as efficient as a dynamic pricing scheme obtained from system optimum conditions and a global optimization with full information about the future states of the system. Stackelberg equilibrium is also investigated in a competitive behavior between different parking facility operators. Policy indications on on-street storage capacity management and pricing are provided.     

A framework to evaluate policy options for supporting electric vehicles in urban freight transport

Electric vehicles (EVs) are considered as a feasible alternative to traditional vehicles. Few studies have addressed the impacts of policies supporting EVs in urban freight transport. To cast light on this topic, we established a framework combining an optimization model with economic analysis to determine the optimal behavior of an individual delivery service provider company and social impacts (e.g., externalities and welfare) in response to policies designed to support EVs, such as purchase subsidy, limited access (zone fee) to congestion/low-emission zones with exemptions for EVs, and vehicle taxes with exemptions for EVs. Numerical experiments showed that the zone fee can increase the company’s total logistics costs but improve the social welfare. It greatly reduced the external cost inside the congestion/low-emission zone with a high population, dense pollution, and heavy traffic. The vehicle taxes and subsidy were found to have the same influence on the company and society, although they have different effects with low tax/subsidy rates because their different effects on vehicle routing plans. Finally, we performed a sensitivity analysis. Local factors at the company and city levels (e.g., types of vehicle and transport network) are also important to designing efficient policies for urban logistics that support EVs.     

The role of volume-delay functions in forecasting and evaluating congestion charging schemes: the Stockholm case

This paper uses observations from before and during the Stockkholm congestion charging trial in order to validate and improve a transportation model for Stockholm. The model overestimates the impact of the charges on traffic volumes while at the same time it substantially underestimates the impact on travel times. These forecast errors lead to considerable underestimation of economic benefits which are dominated by travel time savings. The source of error lies in the static assignment that is used in the model. Making the volume-delay functions (VDFs) steeper only marginally improves the quality of forecast but strongly impacts the result of benefit calculations. We therefore conclude that the dynamic assignment is crucial for an informed decision on introducing measures aimed at relieving congestion. However, in the absence of such a calibrated dynamic model for a city, we recommend that at least a sensitivity analysis with respect to the slope of VDFs is performed.     

Parking policy,parking location decisions and the distribution of congestion

The paper develops and tests a model which characterizes the parking location decisions of individual tripmakers. The model is designed to offer information concerning the effects of alternative parking policies on parking location decisions and therefore the effects on the distribution of congestion in an urban area.Own price, time price and full price elasticities for alternative parking locations are estimated. The own price elasticity is found to rise with distance from the destination point while the time price elasticity falls with distance. The full price elasticity is found to be relatively stable.One is able to determine from the calculated elasticities, the effects of alternative parking policies such as raising parking fees, time restrictions, or increasing search or transaction costs on the distribution of individuals consuming parking services; from this one can infer the impact on the distribution of congestion.The paper also offers some explanation for the low elasticity of auto use with respect to changes in parking costs found in some modal choice studies.The author is indebted to Adolf Buse, Ken Norrie and Richard Westin for helpful comments and criticism.     

Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach

Cycling is often promoted as a means of reducing urban congestion and improving health, social and environmental outcomes. However, the quantification of these potential benefits is not well established. This is due in part to practical difficulties in estimating cycling demand and a lack of sound methodologies to appraise cycling initiatives. In this paper we attempt to address this need by developing predictive models of cycle demand, relative to other transport modes, that capture not only the impacts of observed characteristics such as age and travel time but also the role of attitudes and perceptions. Using data from a stated preference survey, we estimate a hybrid choice model for cycle use that incorporates the role of attitudes towards cycling, perceptions of the image associated with cycling, and the stress arising from safety concerns. Model results indicate that the latent attitudes and perceptions explain an important part of the non-observable utility in a simple multinomial logit choice model. We also demonstrate policy analysis using the hybrid choice model, which allows comparisons of ‘hard’ policies such as the provision of parking facilities against ‘soft’ measures such as cycle promotion schemes.     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.     

Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility

Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research on the impacts of self-driving vehicles on traffic flows, mainly on interurban roads. However, little attention has been given to what could happen to urban mobility when all vehicles are automated. In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs. Automation allows a vehicle to travel without passengers to satisfy multiple household trips and, if needed, to park itself in any of the network nodes to benefit from lower parking charges. Nonetheless, the empty trips can also represent added congestion in the network. The model was applied to a case study based on the city of Delft, the Netherlands. Several experiments were done, comparing scenarios where parking policies and value of travel time (VTT) are changed. The model shows good equilibrium convergence with a small difference between the general costs of traveling for similar families. We were able to conclude that vehicle automation reduces generalized transport costs, satisfies more trips by car and is associated with increased traffic congestion because empty vehicles have to be relocated. It is possible for a city to charge for all street parking and create free central parking lots that will keep total transport costs the same, or reduce them. However, this will add to congestion as traffic competes to access those central nodes. In a scenario where a lower VTT is experienced by the travelers, because of the added comfort of vehicle automation, the car mode share increases. Nevertheless this may help to reduce traffic congestion because some vehicles will reroute to satisfy trips which previously were not cost efficient to be done by car. Placing the free parking in the outskirts is less attractive due to the extra kilometers but with a lower VTT the same private vehicle demand would be attended with the advantage of freeing space in the city center.     

Tackling traffic congestion in Accra,Ghana: A road user's perspective

A fast emerging component of the urban transportation problem in cities of the Third World is the problem of traffic congestion. Rapid increases in car ownership coupled with poor land use planning, inadequate road space, lack of regulated parking systems, uneducated use of the road by pedestrians, and bad driving behavior of motorists have all combined to produce congestions comparable to those experienced in cities in the advanced parts of the world. Traffic management measures have been tried in some of the major cities such as road expansion and redistribution of land uses in city centers, but most have produced minimal results. It is recommended in this paper that city authorities adopt an integrated approach to congestion with emphasis on influencing the behavior of the road users.     

A multivariate state space approach for urban traffic flow modeling and prediction

Urban traffic congestion is one of the most severe problems of everyday life in Metropolitan areas. In an effort to deal with this problem, intelligent transportation systems (ITS) technologies have concentrated in recent years on dealing with urban congestion. One of the most critical aspects of ITS success is the provision of accurate real-time information and short-term predictions of traffic parameters such as traffic volumes, travel speeds and occupancies. The present paper concentrates on developing flexible and explicitly multivariate time-series state space models using core urban area loop detector data. Using 3-min volume measurements from urban arterial streets near downtown Athens, models were developed that feed on data from upstream detectors to improve on the predictions of downstream locations. The results clearly suggest that different model specifications are appropriate for different time periods of the day. Further, it also appears that the use of multivariate state space models improves on the prediction accuracy over univariate time series ones.     

Congestion and variable user charging as an effective travel demand management instrument

Interest at the political level in congestion charging is gaining pace as cities struggle with ways to reduce the effects of growing traffic congestion on the liveability of cities. Despite a long history of promotion of a wide array of travel demand management (TDM) initiatives, very few have had a noticeable impact on the levels of traffic on the road networks of metropolitan areas. TDM success in this context has almost become ‘band-aid’ in the absence of a pricing strategy that not only promotes efficient use of the system but also hypothecates revenues to support essential complementary infrastructure and services such as public transport. This paper takes a look at the stream of pricing consciousness that is surfacing around the world. Although very few jurisdictions have implemented congestion charging, or any form of efficient variable car and truck user charging, the winds of change are well in place. The adage “it is not a matter of if but of when” seems to be the prevailing view. Our overview of global trends in positioning the debate and hopefully follow-through commitment to implementation provides a backdrop to papers submitted for this special issue on travel demand management. The predominance of papers on pricing is indicative of the priority that must be given to efficient charging and revenue disbursement.     

Regulatory reform and the congestion of urban railways

This paper examines various regulatory methods for congested urban railways. After building a closed spatial model with a congestible railway line, we conduct a numerical simulation of the impacts of the regulatory methods, using data on a typical railway firm in Japan. A closed spatial model in this paper enables us to evaluate the regulatory methods in terms of social welfare as well as to show a change in the typical performance of railways, such as the level of fares, railway capacity, and congestion. The simulation results show that implementing simple price-cap regulation, without properly addressing the congestion problem, makes railway firms operate efficiently, but substantially shrinks railway capacity and lowers social welfare. We then consider modified versions of price-cap regulation, which are consistent with relief of congestion.     

Optimal assignment for check-in counters based on passenger arrival behaviour at an airport

Establishing how to utilize check-in counters at airport passenger terminals efficiently is a major concern facing airport operators and airlines. Inadequate terminal capacity and the inefficient utilization of facilities such as check-in counters are major factors causing congestion and delays at airport passenger terminals. However, such delays and congestion can be reduced by increasing the efficiency of check-in counter operations, based on an understanding of passengers' airport access behaviour. This paper presents an assignment model for check-in counter operations, based on passengers' airport arrival patterns. In setting up the model, passenger surveys are used to determine when passengers arrive at the airport terminals relative to their flight departure times. The model then uses passenger arrival distribution patterns to calculate the most appropriate number of check-in counters and the duration of time that each counter should be operated. This assignment model has been applied at the Seoul Gimpo International Airport in Korea. The model provides not only a practical system for the efficient operations of time-to-time check-in counter assignments, but also a valuable means of developing effective longer-term solutions to the problem of passenger terminal congestion and delays. It also offers airlines a means of operating check-in counters with greater cost effectiveness, thus leading to enhanced customer service.     

Empirical assessment of urban traffic congestion

This paper presents an empirical assessment of urban traffic congestion in Central London, UK. Compared with freeways or motorways, urban networks are relatively less studied because of its complexity and availability of required traffic data. This paper introduces the use of automatic number plate recognition technology to analyze the characteristic of urban traffic congestion in Central London. We also present the use of linear regression to diagnose the observed congestion and attribute them to different causes. In particular, we distinguish the observed congestion into two main components: one due to recurrent factors and the other due to nonrecurrent factors. The methodologies are illustrated through a case study of Central London Area. It is found that about 15% of the observed congestion in the region is due to nonrecurrent factors such as accidents, roadwork, special events, and strikes. Given the significance of London, the study will be valuable for transport policy evaluation and appraisal in other global cities. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling urban taxi services in road networks: Progress,problem and prospect

Traditionally, many economists have examined the models and economics of urban taxi services under various types of regulation such as entry restriction and price control in an aggregate way. Only recently have we modeled urban taxi services in a network context. A realistic method has been proposed to describe vacant and occupied taxi movements in a road network and taxi drivers' search behavior for customers. A few extensions have been made to deal with demand elasticity and congestion effects together with development of efficient solution algorithms. Calibration and validation of the network taxi service models have been conducted towards their practical applications. This paper presents an overview of the research that has been carried out by the authors to develop network equilibrium models and solution algorithms for urban taxi services, and offers perspectives for future researches.     

集聚经济视角下轨道交通投资对城市系统均衡的影响

随着城镇化进程加快,城市规模增加所带来的集聚效应和交通拥堵等负外部性之间的矛盾越来越突出,优化城市交通基础设施建设是缓解该矛盾的重要举措。本文同时考虑了集聚经济和交通拥挤两种外部性,构建并求解了一个两区域城市空间结构模型,分三种情况探究了轨道交通的投资引入和道路容量的优化对城市规模和城市空间结构均衡的影响。结果表明,轨道交通的投资引入能够增加城市人口规模和社会福利,使城市结构更加紧凑。但是,随着集聚经济水平的提高,城市规划者应逐步完善公交配套设施,以降低公交拥挤成本,提高公交出行分担率;还应制定相应的福利政策,避免由于个体效用下降而导致人口流出。     

Traffic and emissions impact of congestion charging in the central Beijing urban area: A simulation analysis

Congestion charging is being considered as a potential measure to address the issue of substantially increased traffic congestion and vehicle emissions in Beijing. This study assessed the impact of congestion charging on traffic and emissions in Beijing using macroscopic traffic simulation and vehicle emissions calculation. Multiple testing scenarios were developed with assumptions in different charging zone sizes, public transit service levels and charging methods. Our analysis results showed that congestion charging in Beijing may increase public transit use by approximately 13%, potentially reduce CO and HC emissions by 60–70%, and reduce NOx emissions by 35–45% within the charging zone. However, congestion charging may also result in increased travel activities and emissions outside of the charging zone and a slight increase in emissions for the entire urban area. The size of charging zone, charging method, and charging rate are key factors that directly influence the impact of congestion charging; improved public transit service needs to be considered as a complementary approach with congestion charging. This study is used by Beijing Transportation Environment and Energy Center (BTEC) as reference to support the development of Beijing’s congestion charging policy and regulation.     

Towards marginal cost pricing: A comparison of alternative pricing systems

European urban areas are marred by the problems of congestion and environmental degradation due to the prevailing levels of car use. Strong arguments have thus been put forward in support of a policy based on marginal cost pricing (European Commission 1996). Such policy measures – which would force private consumers to pay for a public service that was previously provided "for free" – are, however, notoriously unpopular with the general public and hence also with their elected representatives – the politicians. There is thus an obvious tension between economic theory, which suggests that marginal cost pricing is the welfare maximising solution to urban transport problems, and practical experience, which suggests that such pricing measures are unwanted by the affected population and hence hard to implement through democratic processes. The AFFORD Project for the European Commission has aimed to investigate this paradox and its possible solutions, through a combination of economic analysis, predictive modelling, attitudinal surveys, and an assessment of fiscal and financial measures within a number of case study cities in Europe. In this paper the methodology and results obtained for the Edinburgh case study are reported in detail. The study analyses alternative road pricing instruments and compares their performance against the theoretical first best situation. It discusses the effect of coverage, location, charging mechanism and interaction with other instruments. The paper shows that limited coverage in one mode may lead to a deviation from the user pays principle in other modes, that location is as important as charge levels and that assumptions about the use of revenues are critical in determining the effect on equity and acceptability. Finally the results show that a relatively simple smart card system can come close to providing the economic first best solution, but that this result should be viewed in the context of the model assumptions.