A methodology to estimate travel time using dynamic traffic assignment (DTA) under incident conditions

This paper presents results from a research case study that examined the distribution of travel time of origin–destination (OD) pairs on a transportation network under incident conditions. Using a transportation simulation dynamic traffic assignment (DTA) model, incident on a transportation network is executed under normal conditions, incident conditions without traveler information availability, and incident conditions assuming that users had perfect knowledge of the incident conditions and could select paths to avoid the incident location. The results suggest that incidents have a different impact on different OD pairs. The results confirm that an effective traveler information system has the potential to ease the impacts of incident conditions network wide. Yet it is also important to note that the use of information may detriment some OD pairs while benefiting other OD pairs. The methodology demonstrated in this paper provides insights into the usefulness of embedding a fully calibrated DTA model into the analysis tools of a traffic management and information center.     

Quantifying traveler information provision in dynamic heterogeneous traffic networks

Information is effectively the same as a change in uncertainty perceived by an observer. This paper adopts the strict definition of information from Shannon’s Information Theory and provides procedures for quantifying effective provision of traveler information, considering it to be equivalent to the change of perceived uncertainty. The proposed method combines a cognitive grouping theory and an information learning scheme at an individual’s level to evaluate the dynamic information provision in the unit of a bit. Such numerical quantification can be meaningful in evaluating alternatives with more fine-grained information provision strategies and understanding their equity impact. Quantifying information in a manner consistent with Information Theory also provides a ‘shared language’ that facilitates more constructive discussion among stakeholders from different backgrounds. The case study is conducted on a heterogeneous dynamic traffic network near Downtown Los Angeles for evaluating different alternatives of a proposed dynamic message board in terms of its location and dynamic content.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

Integrated model for traffic flow forecasting under rainy conditions

Inclement weather, such as heavy rain, significantly affects road traffic flow operation, which may cause severe congestion in road networks in cities. This study investigates the effect of inclement weather, such as rain events, on traffic flow and proposes an integrated model for traffic flow parameter forecasting during such events. First, an analysis of historical observation data indicates that the forecasting error of traffic flow volume has a significant linear correlation with mean precipitation, and thus, forecasting accuracy can be considerably improved by applying this linear correlation to correct forecasting values. An integrated online precipitation‐correction model was proposed for traffic flow volume forecasting based on these findings. We preprocessed precipitation data transformation and used outlier detection techniques to improve the efficiency of the model. Finally, an integrated forecasting model was designed through data fusion methods based on the four basic forecasting models and the proposed online precipitation‐correction model. Results of the model validation with the field data set show that the designed model is better than the other models in terms of overall accuracy throughout the day and under precipitation. However, the designed model is not always ideal under heavy rain conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

A conflict model and interactive simulator (FASTCARS) for predicting enroute driver behavior in response to real-time traffic condition information

This paper proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavioral choice under Advanced Traveler Information Systems (ATIS). The theoretical framework is based on conflict assessment and resolution theories popularized in psychology and applied to models of individual consumer behavior. It is posed that enroute assessment and adjustment is a reactionary process influenced by increased conflict arousal and motivation to change. When conflict rises to a level at which conflict exceeds a personal threshold of tolerance, drivers are likely to alter enroute behavior to alleviate conflict through either route diversion of goal revision. Assessment and response to conflict arousal directly relate to the driver's abilities to perceive and predict network conditions in conjunction with familiarity of network configurations and accessible alternate routes.Data collection is accomplished through FASTCARS (Freeway andArterialStreetTrafficConflictArousal andResolutionSimulator), in interactive microcomputer-based driving simulator. Limited real-world implementation of ATIS has made it difficult to study or predict individual driver reaction to these technologies. It is contended here that in-laboratory experimentation with interactive route choice simulators can substitute for the lack of real-world applications and provide an alternate approach to data collection and driver behavior analysis. This paper will explain how FASTCARS is useful for collecting data and testing theories of driver behavior.     

A study of the influence of severe environmental conditions on common traffic congestion features

On the basis of real traffic and environmental data measured on German freeways, we studied common features of traffic congestion under the influence of severe weather conditions. We have found that traffic features [J] and [S] defining traffic phases “wide moving jam” (J) and “synchronized flow” (S) in Kerner's three‐phase theory are indeed common spatiotemporal traffic features. The quantitative parameters for both traffic phases [S] and [J] were investigated in a comparison of “ideal” weather conditions (good visibility and no precipitation) and severe weather situations (icy road, wind, precipitation, etc.). We showed spatiotemporal congested patterns in several space–time diagrams based on the Automatic Tracking of Moving Jams/Forecasting of Traffic Objects (ASDA/FOTO) model reconstruction for roadside detectors. A statistical study of traffic phase [J] parameters was presented, showing the average values and standard deviation of the quantities. Similarities and differences were analyzed, and some consequences for vehicular applications were discussed to cope with severe weather conditions. Copyright © 2013 John Wiley & Sons, Ltd.     

A macro model for traffic flow on road networks with varying road conditions

In this paper, we develop a macro traffic flow model with consideration of varying road conditions. Our analytical and numerical results illustrate that good road condition can enhance the speed and flow of uniform traffic flow whereas bad road condition will reduce the speed and flow. The numerical results also show that good road condition can smooth shock wave and improve the stability of traffic flow whereas bad road condition will lead to steeper shock wave and reduce the stability of traffic flow. Our results are also qualitatively accordant with empirical results, which implies that the proposed model can qualitatively describe the effects of road conditions on traffic flow. These results can guide traffic engineers to improve the road quality in traffic engineering. Copyright © 2012 John Wiley & Sons, Ltd.     

Real time traffic information and its impacts on route switching behavior of expressway drivers

Non‐quantifiable factors (e.g. perceived, attitudinal and preferential factors) have not been investigated fully in past transportation studies, which has raised questions on the predictive capabilities of the models. In this study, Structure Integration Models, with one of their sub‐models, Measurement Equation, are combined with latent variables, which are integrated with another sub‐model, Structural Equation. The estimated latent variables are used as explanatory variables in decision models. As a result, the explanatory and predictive capabilities of the models are enhanced. The models can then be used to describe the various behaviors of travelers of different types of transportation systems in a more accurate way. In this study, the Structure Integration Model was applied to study the impacts of real‐time traffic information on the route‐switching behavior of road users on the Sun Yat‐Sen expressway, Taiwan. At present, the real‐time traffic information provided on this expressway includes radio traffic reports and changeable message signs. The results of this study can facilitate the provision of traffic information on highways.     

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.     

Modeling the charging and route choice behavior of BEV drivers

Due to the limited cruising range of battery electric vehicle (BEV), BEV drivers show obvious difference in travel behavior from gasoline vehicle (GV) drivers. To analyze BEV drivers’ charging and route choice behaviors, and extract the differences between BEV and GV drivers’ travel behavior, two multinomial logit-based and two nested logit-based models are proposed in this study based on a stated preference survey. The nested structure consists of two levels: the upper level represents the charging decision, and the lower level shows the route choices corresponding to the charging and no-charging situations respectively. The estimated results demonstrate that the nested structure is more appropriate than the multinomial structure. Meanwhile, it is observed that the initial state of charge (SOC) at origin of BEV is the most important factor that affects the decision of charging or not, and the SOC at destination becomes an important impact factor affecting BEV drivers’ route choice behavior. As for the route choice behavior when BEV has charging demand, the charging station attributes such as charging time and charging station’s location have significant influences on BEV drivers’ decision-making process. The results also show that BEV drivers incline to choose the routes with charging station having less charging time, being closer to origin and consistent with travel direction. Finally, based on the proposed models, a series of numerical analysis has been conducted to verify the effect of range anxiety on BEV charging and route choice behavior and to reveal the variation of comfortable initial SOC at origin with travel distance. Meanwhile, the effects of charging time and distance from origin to charging station also have been discussed.     

Choice valuation of traffic restrictions: Noise, pollution, and congestion preferences. A note

This Note looks at the choice valuation restrictions on traffic entering the city of Lisbon, based on individual preferences in relation to noise, pollution and congestion. The analysis employs a questionnaire distributed in 2007 to ascertain the significant characteristics of traveling to Lisbon, with the aim of curbing the number of cars that are associated with the probability of individuals supporting a charge on motor vehicles entering the city. The model also takes into account the uncontrolled heterogeneity of the data.     

Optimization method of alternate traffic restriction scheme based on elastic demand and mode choice behavior

As a countermeasure to urban traffic congestion, alternate traffic restriction (ATR) involves a certain proportion of automobiles being prohibited from entering pre-determined ATR districts during specific time periods. The present study introduces an optimization method for ATR schemes in terms of both their restriction districts and the proportion of restricted automobiles. As a Stackelberg game between traffic policy makers and road users, the ATR scheme optimization problem is established using a bi-level programming model, with the upper-level examining an ATR scheme aimed at consumers’ surplus maximization under the condition of overload flow minimization, and the lower-level synthetically optimizing elastic demand, mode choice (private car, public transit and park-and-ride) and multi-class user equilibrium assignment. A genetic algorithm based on the graph theory is also proposed to solve the bi-level programming model with a gradient project algorithm for solving the lower-level model. To our knowledge, this study represents the first attempt to theoretically optimize an ATR scheme using a systematic approach with mathematical model specification.     

Analyzing the impact of a planned transit-oriented development on mode share and traffic conditions

ABSTRACT

Transit-oriented development (TOD) is a popular planning strategy used to maximize accessibility to transit for various trip purposes. The quantitative effects of TOD on travel mode shift and traffic congestion have not been extensively tested in the current literature. This paper utilizes a seemingly unrelated regressions (SUR) mode share model and a mesoscopic dynamic traffic assignment (DTA) model to analyze the impact of a planned TOD in Maryland. The proposed model aims at improving the understanding of the quantitative impacts of such a TOD on mode share and traffic congestion. The main result of the mode share model indicates that the increase in transit ridership for a transit accessible shopping center is not that significant. Local traffic conditions will deteriorate due to a lack of investment in road infrastructure planned for the TOD area. The proposed method could be a valuable tool for other indicative land development or transportation policy analyses.     

Modal analysis of real-time,real world vehicular exhaust emissions under heterogeneous traffic conditions

This study presents the characteristics of real world, real time, on-road vehicular exhaust emission namely, carbon monoxide (CO), nitric oxide (NO), hydrocarbons (HC), and carbon dioxide (CO₂) emitted under heterogeneous traffic conditions. Field experiments were performed on major category of vehicles in developing countries, i.e. two-wheelers, auto-rickshaws, cars and buses. The on-board monitoring was carried out on different corridors with varying road geometry. Results revealed that the driving cycle was dependent on the road geometry, with two lane mixed flow corridor having lot of short term events compared to that of arterial road. Vehicular emissions during idling and cruising were generally low compared to emissions during acceleration. It was also found that emissions were significantly dependent on short term events such as rapid acceleration and braking during a trip. Also, the standard emission models like COPERT and CMEM under predicted the real world emissions by 30–200% depending upon different driving modes. The on-road emissions measurements were able to capture the emission characteristics during the micro events of real world driving scenarios which were not represented by standard vehicle emission measured at laboratory conditions.     

A porous flow approach to modeling heterogeneous traffic in disordered systems

A continuum model that describes a disordered, heterogeneous traffic stream is presented. Such systems are widely prevalent in developing countries where classical traffic models cannot be readily applied. The characteristics of such systems are unique since drivers of smaller vehicles exploit their maneuverability to move ahead through lateral gaps at lower speeds. At higher speeds, larger vehicles press their advantage of greater motive power. The traffic stream at the microscopic level is disordered and defines a porous medium. Each vehicle is considered to move through a series of pores defined by other vehicles. A speed-density relationship that explicitly considers the pore space distribution is presented. This captures the considerable dynamics between vehicle classes that are overlooked when all classes are converted to a reference class (usually Passenger Car Equivalents) as is traditionally done. Using a finite difference approximation scheme, traffic evolution for a two-class traffic stream is shown.     

Behavior of taxi customers in hailing vacant taxis: a nested logit model for policy analysis

This study models and examines the taxi customers' preferences for hailing vacant taxis on streets. A stated preference survey was conducted to randomly select and interview 1242 taxi customers at taxi stands and pedestrians on streets, who had experiences of taking taxis recently, about their choices under different given hypothetical scenarios. In total, 4968 observations were collected and used for developing the discrete choice models for the analysis. To account for the potential correlations among alternatives, two nested logit models are developed, calibrated, and compared with a standard multinomial logit model in the investigation. The results of likelihood ratio test demonstrate that one of the developed nested logit models is better than the standard multinomial logit model to describe the search behavior of taxi customers. The model results also show that the walking time to and the waiting time at the location for hailing taxis, the extra travel time to the destination because of local circulation for finding a way from the pickup location heading to a passenger's destination, as well as the taxi customers' perceptions for walking to and waiting at taxi stands were found as significant factors to influence their decisions. In addition, the results of market segmentation analysis illustrate the variations in taxi‐search strategies of taxi customers in different districts and regions. Some policy implications on introducing more taxi stands and improving the utilization rates of taxi stands are also discussed. We believe that the proposed models, findings, and discussion are useful for developing micro‐simulation models to evaluate the performance of road traffic networks with taxi services and developing simulation‐based optimization models to answer policy questions related to taxi services. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling heavy vehicle car‐following behaviour in congested traffic conditions

This study develops a car‐following model in which heavy vehicle behaviour is predicted separately from passenger car. Heavy vehicles have different characteristics and manoeuvrability compared with passenger cars. These differences could create problems in freeway operations and safety under congested traffic conditions (level of service E and F) particularly when there is high proportion of heavy vehicles. With increasing numbers of heavy vehicles in the traffic stream, model estimates of the traffic flow could be degrades because existing car‐following models do not differentiate between these vehicles and passenger cars. This study highlighted some of the differences in car‐following behaviour of heavy vehicle and passenger drivers and developed a model considering heavy vehicles. In this model, the local linear model tree approach was used to incorporate human perceptual imperfections into a car‐following model. Three different real world data sets from a stretch of freeway in USA were used in this study. Two of them were used for the training and testing of the model, and one of them was used for evaluation purpose. The performance of the model was compared with a number of existing car‐following models. The results showed that the model, which considers the heavy vehicle type, could predict car‐following behaviour of drivers better than the existing models. Copyright © 2013 John Wiley & Sons, Ltd.     

A novel approach for assessing the LOS for two‐lane intercity highways under heterogeneous traffic conditions

Performance of two‐lane intercity highways has been evaluated in terms of level of service (LOS) by different researchers. Different follower‐related performance measures, namely, the number of followers (NF), percent followers (PF), follower density (FD) and the number of followers as a proportion of capacity (NFPC) are examined in the present study to define LOS. Data are collected from five sites located in different parts of India. While almost all the past studies used 3‐s headway rule to identify followers suggested by US Highway Capacity Manual, a new methodology is proposed in the current study to identify the followers by analysing speed difference (SD) and the gap between two consecutive vehicles. It is observed that vehicles travel in non‐following condition after a critical gap threshold value of 10 s. By using a SD limit of ?4 km/h to +10 km/h and a gap value of 10 s, followers are identified across all the study sites. Thereafter, different critical gap values ranging from 1.9 s to 4.3 s are observed at the study sites beyond which the probability of not following would increase. Variation in two‐way traffic volume is found to be the main contributory factor which affects the critical gap values. Among all of the performance measures, NFPC shows a strong correlation with two‐way traffic volume followed by FD under heterogeneous traffic condition. Finally, different threshold values of LOS ranges for two‐lane intercity highways are provided by carrying out cluster analysis with the help of NFPC and FD. Copyright © 2017 John Wiley & Sons, Ltd.