Multimodal,multiclass stochastic dynamic traffic assignment for evaluating information provision strategies

A multimodal, multiclass stochastic dynamic traffic assignment model was developed to evaluate pre‐trip and enroute travel information provision strategies. Three different information strategies were examined: user optimum [UO], system optimum [SO] and mixed optimum [MO]. These information provision strategies were analyzed based on the levels of traffic congestion and market penetration rate for the information equipment. Only two modes, bus and car, were used for evaluating and calculating the modal split ratio. Several scenarios were analyzed using day‐to‐day and within day dynamic models. From the results analyzed, it was found that when a traffic manager provides information for drivers using the UO strategy and drivers follow the provided information absolutely, the total travel time may increases over the case with no information. Such worsening occurs when drivers switch their routes and face traffic congestion on the alternative route. This phenomenon is the 'Braess Paradox'.     

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications.     

Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature

Abstract

This paper explores social media's role in managing unplanned transit networks disruptions. Although literature exists more broadly on the use of social media in transit, this paper presents the first literature review in this setting. When disruptions occur, commuters require reliable, up-to-date information. Its provision reduces anxiety and allows informed choices. Social media is beneficial given it provides real-time information but it can only supplement (not replace) conventional approaches. Information reliability was critical. Research in the field of disaster management illustrates the importance of publicly contributed information. Known as “crowdsourcing”, it is part of the emerging field of crisis informatics which for the first time was linked to unplanned transit disruption management. The results highlight that social media's real-time nature can reduce disrupted travel demand; however, its utilisation can be resource-intensive. A framework presented illustrates how social media utilisation varies according to the operational characteristics of a disrupted network. Social media use as an information delivery tool is still in its infancy and an unwillingness to embrace it is an impediment to sustained growth. Crowdsourcing is one approach that could resolve the issue of transit agency resourcing whilst satisfying the increased demand and expectation for real-time information.     

From willingness to action: Do push-pull-mooring factors matter for shifting to green transportation?

Motivating individuals to choose green transportation is becoming increasingly important. Based on push-pull-mooring framework, this study aims to explore how push, pull and mooring factors foster individual’s willingness to shift to green transportation. This study also analyzes the role of information provision in narrowing the gap between shifting willingness and behavior. The findings revealed that push factors, including perceived environmental threats and perceived inconvenience, drive individual’s mode-shift away from private cars, whereas the pull factors, including green transport policies and campaigns, and green transport system attract individual’s mode-shift to green transportation. Moreover, the mooring factor, namely inertia, not only negatively affects individual’s shifting willingness but also negatively moderates the effects of push and pull factors on individual’s shifting willingness. In addition, shifting willingness positively affects the shifting behavior and the information provision positively moderates the relationship among them. Such findings are vital to achieve the realization of China’s low-carbon goals.     

Learning-based framework for transit assignment modeling under information provision

The modeling of service dynamics has been the focus of recent developments in the field of transit assignment modeling. The emerging focus on dynamic service modeling requires a corresponding shift in transit demand modeling to represent appropriately the dynamic behaviour of passengers and their responses to Intelligent Transportation Systems technologies. This paper presents the theoretical development of a departure time and transit path choice model based on the Markovian Decision Process. This model is the core of the MIcrosimulation Learning-based Approach to TRansit Assignment. Passengers, while traveling, move to different locations in the transit network at different points in time (e.g. at stop, on board), representing a stochastic process. This stochastic process is partly dependent on the transit service performance and partly controlled by the transit rider’s trip choices. This can be analyzed as a Markovian Decision Process, in which actions are rewarded and hence passengers’ optimal policies for maximizing the trip utility can be estimated. The proposed model is classified as a bounded rational model, with a constant utility term and a stochastic choice rule. The model is appropriate for modeling information provision since it distinguishes between individual’s experience with the service performance and information provided about system dynamics.     

Using a weight-assessing model to identify route choice criteria and information effects

Shortest-path (minimum travel time) routing has been adopted over the past few decades. However, many studies have shown that a driver’s route and the shortest path differ widely in significant ways, and that most drivers use several criteria in selecting their routes. Since route choice criteria have been the subject of controversy, this study develops an individual behavioral-based mechanism for exploring the crucial criteria affecting drivers’ route-selection decisions. On the basis of the weight-assessing model and the habitual domain theory, this study presents the dynamic change of route choice criteria according to their dynamic weights. Furthermore, the effects of information on drivers’ route-formulating behaviors are investigated as well in order to provide some valuable suggestions for implementing Advanced Traveler Information Systems (ATIS) in the future. An empirical study in Taipei City was conducted to show the feasibility and applicability of our proposed method and the empirical results indicate excellent performance in practice.     

Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers’ trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers’ choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers’ opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers’ preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers’ preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers’ behavioral tendencies concerning toll road utilization in support of traffic information dissemination.     

Effect of traffic information on commuters' propensity to change route and departure time

Considerable public and private resources are devoted to the collection and dissemination of real-time traffic information in the Chicago area. Such information is intended to help individuals make more informed travel decisions, yet its effect on behavior remains largely unexplored. This study evaluates the effect of traffic information on travelers' route and departure time changes and provides a stronger basis for developing advanced information systems. Downtown Chicago automobile commuters were surveyed during the AM peak period. The results indicate that a majority of the respondents access, use and respond to information. For example, individuals use travel information to reduce their anxiety—even if they do not change travel decisions; this indicates that information may have “intrinsic” value. That is, simply knowing traffic conditions is valued by travelers. More than 60% of the respondents had used traffic information to modify their travel decisions. Multivariate analysis using the ordered probit model showed that individuals were more likely to use traffic reports for their route changes if they perceived traffic reports to be accurate and timely, and frequently listened to traffic reports. Respondents were more likely to change their departure times if they perceived traffic reports to be accurate and relevant, and frequently listened to traffic reports. The implication for Advanced Traveler Information Systems are that they may be designed to support both enroute and pre-trip decisions. ATIS performance, measured in terms of accuracy, relevance and timeliness would be critical in the success of such systems. Further, near-term prediction of traffic conditions on congested and unreliable routes (where conditions change rapidly) and incident durations is desirable.     

On the value of highway travel time information systems

After the widespread deployment of Advanced Traveler Information Systems, there exists an increasing concern about their profitability. The costs of such systems are clear, but the quantification of the benefits still generates debate. This paper analyzes the value of highway travel time information systems. This is achieved by modeling the departure time selection and route choice with and without the guidance of an information system. The behavioral model supporting these choices is grounded on the expected utility theory, where drivers try to maximize the expected value of their perceived utility. The value of information is derived from the reduction of the unreliability costs as a consequence of the wiser decisions made with information. This includes the reduction of travel times, scheduling costs and stress. This modeling approach allows assessing the effects of the precision of the information system in the value of the information.Different scenarios are simulated in a generic but realistic context, using empirical data measured on a highway corridor accessing the city of Barcelona, Spain. Results show that travel time information only has a significant value in three situations: (1) when there is an important scheduled activity at the destination (e.g. morning commute trips), (2) in case of total uncertainty about the conditions of the trip (e.g. sporadic trips), and (3) when more than one route is possible. Information systems with very high precision do not produce better results. However, an acceptable level of precision is completely required, as information systems with very poor precision may even be detrimental. The paper also highlights the difference between the user value and the social value of the information. The value of the information may not benefit only the user. For instance, massive dissemination of travel time information contributes to the reduction of day-to-day travel time variance. This favors all drivers, even those without information. In these situations travel time information has the property that its social benefits exceed private benefits (i.e. information has positive externalities). Of course, drivers are only willing to cover costs equal or smaller than their private benefits, which in turn may justify subsidies for information provision.     

Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas

This paper investigates the effects of the provision of traffic information on toll road usage based on a stated preference survey conducted in central Texas. Although many researchers have studied congestion pricing and traffic information dissemination extensively, most of them focused on the effects that these instruments individually produce on transportation system performance. Few studies have been conducted to elaborate on the impacts of traffic information dissemination on toll road utilization. In this study, 716 individuals completed a survey to measure representative public opinions and preferences for toll road usage in support of various traffic information dissemination classified by different modes, contents, and timeliness categories. A nested logit model was developed and estimated to identify the significant attributes of traffic information dissemination, traveler commuting patterns, routing behavior, and demographic characteristics, and analyze their impacts on toll road utilization. The results revealed that the travelers using dynamic message sign systems as their primary mode of receiving traffic information are more likely to choose toll roads. The potential toll road users also indicated their desire to obtain traffic information via internet. Information regarding accident locations, road hazard warnings, and congested roads is frequently sought by travelers. Furthermore, high-quality congested road information dissemination can significantly enhance travelers’ preferences of toll road usage. Specifically the study found that travelers anticipated an average travel time saving of about 11.3 min from better information; this is about 30 % of travelers’ average one-way commuting time. The mean value of the time savings was found to be about $11.82 per hour, close to ½ of the average Austin wage rate. The model specifications and result analyses provide in-depth insights in interpreting travelers’ behavioral tendencies of toll road utilization in support of traffic information. The results are also helpful to shape and develop future transportation toll system and transportation policy.     

Personalized real-time traffic information provision: Agent-based optimization model and solution framework

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time congested traffic management in a congested network. This paper proposes an agent-based optimization modeling framework to provide personalized traffic information for heterogeneous travelers. Based on a space–time network, a time-dependent link flow based integer programming model is first formulated to optimize various information strategies, including elements of where and when to provide the information, to whom the information is given, and what alternative route information should be suggested. The analytical model can be solved efficiently using off-the-shelf commercial solvers for small-scale network. A Lagrangian Relaxation-based heuristic solution approach is developed for medium to large networks via the use of a mesoscopic dynamic traffic simulator.     

Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders

In order to attract more choice riders, transit service must not only have a high level of service in terms of frequency and travel time but also must be reliable. Although transit agencies continuously work to improve on-time performance, such efforts often come at a substantial cost. One inexpensive way to combat the perception of unreliability from the user perspective is real-time transit information. The OneBusAway transit traveler information system provides real-time next bus countdown information for riders of King County Metro via website, telephone, text-messaging, and smart phone applications. Although previous studies have looked at traveler response to real-time information, few have addressed real-time information via devices other than public display signs. For this study, researchers observed riders arriving at Seattle-area bus stops to measure their wait time while asking a series of questions, including how long they perceived that they had waited.The study found that for riders without real-time information, perceived wait time is greater than measured wait time. However, riders using real-time information do not perceive their wait time to be longer than their measured wait time. This is substantiated by the typical wait times that riders report. Real-time information users say that their average wait time is 7.5 min versus 9.9 min for those using traditional arrival information, a difference of about 30%. A model to predict the perceived wait time of bus riders was developed, with significant variables that include the measured wait time, an indicator variable for real-time information, an indicator variable for PM peak period, the bus frequency in buses per hour, and a self-reported typical aggravation level. The addition of real-time information decreases the perceived wait time by 0.7 min (about 13%).A critical finding of the study is that mobile real-time information reduces not only the perceived wait time, but also the actual wait time experienced by customers. Real-time information users in the study wait almost 2 min less than those arriving using traditional schedule information. Mobile real-time information has the ability to improve the experience of transit riders by making the information available to them before they reach the stop.     

Evaluation of simulation uncertainty in accident reconstruction via combining Response Surface Methodology and Monte Carlo Method

This paper focuses on the uncertainty of simulation results in accident reconstruction. Since the Monte Carlo Method (MCM) requires a large number of simulation runs, in order to reduce the simulation time of MCM in evaluating the uncertainty of simulation results, a new method named “Response Surface-Monte Carlo Method (RS-MCM)” was proposed. Firstly, Response Surface Methodology (RSM) was used to obtain an approximate model of the true accident simulation model, and then the uncertainty of simulation results was evaluated by combining this approximate model and MCM. The steps of RS-MCM include the generation of sample sets, the determination of response surface model and the statistical analysis of simulation results. The distribution of reconstruction results was obtained using RS-MCM, which can provide more comprehensive information in traffic accident survey, such as the probability of a simulation result at any given confidence interval falling within an arbitrary interval and so on. Finally, three cases have been employed to evaluate the effectiveness of the proposed RS-MCM.     

Prospect Theory for joint time and money consequences in risk and ambiguity

Transport users face complex decisions. Not only are the consequences of their choices uncertain, but they generally involve several attributes, such as time and money. Time-money tradeoffs have been studied in depth in transport economics, and research is now paying increasing attention to the role of uncertainty and information in transport decisions. This paper aims to measure the impact of uncertainty and information on multi-attribute decisions using Prospect Theory. In doing so, the study makes two contributions to transportation literature: one methodological and the other empirical. First, we propose a fast and tractable method for measuring Prospect Theory parameters that capture attitudes towards probabilities (probability weighting function) and attitudes towards losses (loss aversion). The elicitation method does not require the elicitation of the utility function. This makes it particularly suitable in complex multi-attribute decisions where the shape of the utility function is unknown. Second, we present the results of an experiment that uses the proposed method to measure, at the individual level, probability weighting in decisions involving joint time and money consequences in two decision contexts: risk (where probabilities are given) and ambiguity (where the probability distribution is unknown). An experimental setup that exposes subjects to real gains and losses for money and time has been built for this purpose. We observe inverse S-shaped probability weighting and loss aversion for risk. Probability weighting is even more pronounced in ambiguity, where subjects do not have precise information about the probability distribution. We explain how these results and the analysis of ambiguity attitudes in general can offer a better understanding of travelers’ route or transport mode choices.     

Evaluation of exclusive bus lanes in a bi-modal degradable road network

In this paper, we proposed an evaluation method of exclusive bus lanes (EBLs) in a bi-modal degradable road network with car and bus transit modes. Link travel time with and without EBLs for two modes is analyzed with link stochastic degradation. Furthermore, route general travel costs are formulated with the uncertainty of link travel time for both modes and the uncertainty of waiting time at a bus stop and in-vehicle congestion costs for the bus mode. The uncertainty of bus waiting time is considered to be relevant to the degradation of the front links of the bus line. A bi-modal user equilibrium model incorporating travelers’ risk adverse behavior is proposed for evaluating EBLs. Finally, two numerical examples are used to illustrate how the road degradation level, travelers’ risk aversion level and the front link’s correlation level with the uncertainty of the bus waiting time affect the results of the user equilibrium model with and without EBLs and how the road degradation level affects the optimal EBLs setting scheme. A paradox of EBLs setting is also illustrated where adding one exclusive bus lane may decrease share of bus.     

A monopolistic market for advanced traveller information systems and road use efficiency

Advanced traveller information systems (ATIS) are likely to exhibit significant economies of scale in production and operation. Private provision would therefore typically occur under considerable market power. An important policy question is whether the resulting distortions would aggravate or reduce distortions in the transport market itself, notably external effects such as congestion. We consider such questions by presenting an integrated model that captures the interactions between a congested transport market and a monopolistic market for advanced traveller information systems (ATIS). Three market failures operate simultaneously: congestion on the road, a declining average benefit of information when information penetration rises, and monopolistic pricing by the provider of information. Some key results are as follows. Monopoly information pricing appears not to be the most attractive option from a system efficiency viewpoint. A subsidy in the information market can help realise a second-best optimum of road use. Relatively low uncertainty on the road and high information costs limit the monopolist’s profit on the information market, as well as relative system efficiency. While relatively inelastic demand for mobility, counter intuitively, negatively affects the monopolist’s profit, the relative social benefits from private information peak at intermediate demand elasticities.     

Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions

In this study, to incorporate realistic discrete stochastic capacity distribution over a large number of sampling days or scenarios (say 30–100 days), we propose a multi-scenario based optimization model with different types of traveler knowledge in an advanced traveler information provision environment. The proposed method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions (and related reliability measure) across a large number of different sampling days. Using a gap function framework or describing the mixed user equilibrium under different information availability over a long-term steady state, a nonlinear programming model is formulated to describe the route choice behavior of the perfect information (PI) and expected travel time (ETT) user classes under stochastic day-dependent travel time. Driven by a computationally efficient algorithm suitable for large-scale networks, the model was implemented in a standard optimization solver and an open-source simulation package and further applied to medium-scale networks to examine the effectiveness of dynamic traveler information under realistic stochastic capacity conditions.     

Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems

We consider a specific advanced traveler information systems (ATIS) whose objective is to reduce drivers’ travel time uncertainty with recurrent network congestion through provision of traffic information. Since the provided information is still partial or imperfect, drivers equipped with an ATIS cannot always find the shortest travel time route and thus may not always comply with the advice provided by ATIS. Thus, there are three classes of drivers on a specific day: drivers without ATIS, drivers with ATIS but without compliance with ATIS advice, drivers with ATIS and in compliance with ATIS advice. All three classes of drivers make route choice in a stochastic manner, but with different degree of uncertainty of travel time on the network. In this paper we investigate the interactions among the three classes of drivers in an ATIS environment using a multiple behavior stochastic user equilibrium model. By assuming that the market penetration of ATIS is an increasing function of the actual private gain (time saving minus the cost associated with system use) derived from ATIS service, and the ATIS compliance rate of equipped drivers is given as the probability of the actual travel time of complied drivers being less than that of non-complied drivers, we determine the equilibrium market penetration and compliance rate of ATIS and the resulting equilibrium network flow pattern using an iterative solution procedure.     

Information provision strategies eliminating deluded equilibrium caused by travellers’ misperception

Providing travel time information may be effective at reducing travel costs. However, this information does not always match the actual travel time that travellers will experience. Furthermore, the information is often asymmetrically provided within the network, owing to the limitations of observation devices, prediction model calibration, and uncertainty about road conditions. The purpose of this study is to investigate the effects of predictive travel time information that is asymmetrically provided to travellers. This study formulated a dynamic traffic assignment model in origin–destination (OD) pair with two parallel routes, while considering travellers’ learning processes and within-day and day-to-day dynamics. In this study, it is assumed that different information will be provided to each traveller, according to within-day traffic dynamics. Furthermore, the information is provided for only one of two possible routes, because of observation limitations. The effects of information accuracy are also discussed in this study. The results of numerical analysis indicated that information provisions possibly reduced the negative effects of deluded equilibrium state, even when the information was only provided for one of the routes. Different effects of the travel time information and its variation were illustrated according to the allocation of the bottleneck capacities of two routes.