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.     

The effectiveness of differential pricing on route choice

The use of differential pricing as a means of traffic management has been advocated by specialists in the field of transport for quite some time. Because of technical and political reasons, a full-scale pricing scheme for the use of road space has yet to be introduced. Applying the principle to a highly automatic rail system, however, is technologically much more feasible. The Mass Transit Railway Corporation of Hong Kong has implemented a so-called revenue neutral peal pricing policy after the completion of a second cross-harbour route- the East Harbour Crossing — in May, 1990. Passengers travelling from Kowloon to the central business district (including Jordon and Tsim Sha Tsui on the Kowloon side and stations from Sheung Wan to Causeway Bay on the Island side) during the morning peak hour are confronted with the following choice: either (i) make use of the Nathan Road Corridor and pay 80 cents on top of normal face; or (ii) take the less congested but in general longer route via the East Harbour Crossing and get a 80 cents discount. The present paper attempts to analyse the effectiveness of this differential pricing policy in diverting passengers from the overcrowded section to the less heavily utilized route. A personal interview survey comprising a total of 1094 successful cases was conducted for this purpose. The logit regression model was employed to analyse the route choice. It is found that income, habit and journey time are the most important variables determining the route choice. The effect of cost or fare difference, although large in terms of magnitude, is only marginally significant in the statistical sense. It is suggested that efforts to change the passengers' habit and measures to shorten the train transfer time at the Quarry Bay Station for the East harbour Crossing users would be more effective in achieving this end. This is especially the case given the current political development in Hong King which renders further enlargement of the price difference a highly difficult proposition.     

Route choice preferences: insights from Portuguese freight forwarders and truck drivers

ABSTRACT

Exploring route choice in the context of tolled alternatives can support road operators to achieve better utilization of the infrastructure, as well as maximizing revenue collection. The research presented in this paper is conducted in the context of OPTIMUM, a European Union-funded project. The research objectives include a two-component system of models that proactively calculates commercial vehicles’ toll prices. The component presented in this paper rests on the development of a route choice model that estimates the probabilities of using two alternative routes (toll road vs. national road), based on route attributes and user characteristics. To explore the usefulness of the proposed methodology a case study involving 50 truck drivers and 25 freight operators was conducted in Portugal between January 2016 and November 2017. Results from the route choice model reveal interesting insights about the role of incentives in the choice of toll roads, the perspectives of the different decision-makers and produce Values of Time for the study area.     

Validating the results of a route choice simulator

This paper describes the validation of a route choice simulator known as VLADIMIR (Variable Legend Assessment Device for Interactive Measurement of Individual Route choice). VLADIMIR is an interactive computer-based tool designed to study drivers’ route choice behaviour. It has been extensively used to obtain data on route choice in the presence of information sources such as Variable Message Signs or In-Car Navigation devices. The simulator uses a sequence of digitized photographs to portray a real network with junctions, links, landmarks and road signs. Subject drivers are invited to make journeys between specified origins and destinations under a range of travel scenarios, during which the simulator automatically records their route choices. This paper describes validation experiments carried out during the period Summer 1994 to Autumn 1995 and reports on the results obtained. Each experiment involved a comparison of routes selected in real life with those driven under simulated conditions in VLADIMIR. The analysis included investigation of the subjects’ own assessment of the realism of the VLADIMIR routes they had chosen, a comparison of models based on the real life routes with models based on VLADIMIR routes, and a statistical comparison of the two sets of routes. After an extensive series of data collection exercises and analyses, we have concluded that a well designed simulator is able to replicate real life route choices with a very high degree of detail and accuracy. Not only was VLADIMIR able to precisely replicate the route choices of drivers who were familiar with the network but it also appears capable of representing the kind of errors made and route choice strategies adopted by less familiar drivers. Furthermore, evidence is presented to suggest that it can accurately replicate route choice responses to roadside VMS information.     

Exploration of day-to-day route choice models by a virtual experiment

This paper examines existing day-to-day models based on a virtual day-to-day route choice experiment using the latest mobile Internet technologies. With the realized day-to-day path flows and path travel times in the experiment, we calibrate several well-designed path-based day-to-day models that take the Wardrop’s user equilibrium as (part of) their stationary states. The nonlinear effects of path flows and path time differences on path switching are then investigated. Participants’ path preferences, time-varying sensitivity, and learning behavior in the day-to-day process are also examined. The prediction power of various models with various settings (nonlinear effects, time-varying sensitivity, and learning) is compared. The assumption of “rational behavior adjustment process” in Yang and Zhang (2009) is further verified. Finally, evolutions of different Lyapunov functions used in the literature are plotted, and no obvious diversity is observed.     

The acceptability of road pricing in Vienna: the preference patterns of car drivers

The acceptability of road pricing has attracted considerable attention among researchers over the last decade, as is evident in the amount of literature about transport and environmental economics. The general conclusion from these studies has been that there is low acceptability for road pricing among car users. In this paper, we add more knowledge to the existing literature by conducting an acceptability study of road pricing in Vienna, where such a study has never been conducted before. We used a replication study approach where a previous approach used in the EU research project AFFORD (acceptability of fiscal and financial measures and organisational requirements for demand management) was replicated for Vienna and further supplemented with a conjoint analysis. In order to examining whether the Vienna study confirms previous findings. We investigated the acceptability of two concrete policy packages factors influencing this acceptability, and preference patterns that can be used in designing a road pricing policy for Vienna. The survey reveals a higher acceptability if road pricing schemes lead to perceived personal benefits. According to the multivariate analyses, the “personal outcome expectations”, “social norm” and “perceived effectiveness” variables account for more than 50 % of the criterion variance and therefore these are the most influential factors. Road pricing schemes can be an effective transport management instrument for a city particularly if they are associated with direct investment in public transport and public infrastructure. Thus, personal benefits can be perceived more easily and direct effects can be expected.     

The influence of route guidance advice on route choice in urban networks

The paper begins by reviewing what is known about route choice processes and notes the mismatch between this knowledge and the route choice assumptions embedded in the most widely used assignment models. Empirical evidence on the influence of route guidance advice on route choice is reviewed and, despite its limited nature, is seen to suggest that users are reluctant to follow advice unless they find it convincing and that, the more familiar they are with the network, the less likely they are to accept advice. Typically only a small minority of journeys are made in total compliance with advice.Results from an interactive route choice simulator (IGOR) are summarised and are seen to reveal that compliance depends on the extent to which the advice is corroborated by other factors, on the drivers' familiarity with the network and on the quality of advice previously received. It is noted that the IGOR results are in a form which would enable response models to be calibrated.Recent approaches to the modelling of route choice in the context of guidance are discussed. Some are seen to make simplifying assumptions which must limit the relevance of their results; most make no allowance for the fact that drivers are unlikely to comply with all advice and several are not able to represent the benefits which guidance might bring in the context of sporadic congestion or incidents.As an alternative, a two phase model comprising a medium term strategic equilibrium and a day-specific simulation with explicit representation of driver response is proposed.Updated and extended from an earlier version published in theProceedings of the Japan Society of Civil Engineers (JSCE No 425/IV-4, 1991-1).     

The influence of transport on industrial location choice: a stated preference experiment

Stated preference experiments are introduced and applied to an investigation of the influence of road transport and other factors on industrial location in terms of the ex ante decision making process. The experiments, based upon repeated hypothetical discrete choices between pairs of locations, involved respondents from firms making trade-offs between the various characteristics in a fractional factorial, orthogonal survey design. In each defined case, a clear hierarchy of location factors emerged. These were found to vary according to the origin of the firm – classified as local relocations, foreign inward investors, and branch plants sourced from national bases. The importance of road links to location choice varied considerably between these groups with the latter rating motorway links the highest of any of the groups of firms. In contrast, overseas sourced branch firms found road links largely unimportant, being outweighed primarily by considerations of workforce and premises. Local relocations fell into two distinct groups with respect to the importance attached to road links (between relatively important and non-important), whilst considering the other factors similarly. Good public transport provision emerged as a statistically significant factor only in certain scenarios. Finally, the paper discusses implications for location choice models in transport and further research.     

Estimating commuter mode choice: A discrete choice analysis of the impact of road pricing and parking charges

Automobile use leads to external costs associated with emissions, congestion, noise and other impacts. One option for minimizing these costs is to introduce road pricing and parking charges to reduce demand for single occupant vehicle (SOV) use, while providing improvements to alternatives to encourage mode switching. However, the impact of these policies on urban mode choice is uncertain, and results reported from regions where charging has been introduced may not be transferable. In particular, revealed preference data associated with cost recovery tolls on single facilities may not provide a clear picture of driver response to tolls for demand management. To estimate commuter mode choice behaviour in response to such policies, 548 commuters from a Greater Vancouver suburb who presently drive alone to work completed an individually customized discrete choice experiment (DCE) in which they chose between driving alone, carpooling or taking a hypothetical express bus service when choices varied in terms of time and cost attributes. Attribute coefficients identified with the DCE were used in a predictive model to estimate commuter response to various policy oriented combinations of charges and incentives. Model results suggest that increases in drive alone costs will bring about greater reductions in SOV demand than increases in SOV travel time or improvements in the times and costs of alternatives beyond a base level of service. The methods described here provide an effective and efficient way for policy makers to develop an initial assessment of driver reactions to the introduction of pricing policies in their particular regions.     

Distinguishing the land use effects of road pricing based on the urban form attributes

This paper studies the effects of road pricing on land use under different development scenarios (business as usual scenario and transit oriented development scenario) by a quantitative method, which combines the integrated land use and transport interaction model (TRANUS model) with the scenario-planning techniques. Moreover, in order to further analyze the differences of the land use effects of road pricing on traffic analysis zones (TAZs) with different urban form attributes, a quantitative classification method combining factor analysis and cluster analysis is then used to quantitatively classify TAZs. The results demonstrate that the effects of road pricing on the land use of a specific region depend on the urban form attributes of the region. The higher the densities of employments and population, and better street design (high densities of street and intersections) and public transportation condition, the less the region is negatively affected by road pricing, and vice versa. More importantly, rail transit can alleviate the negative impact of road pricing on commercial development and population concentration of the region. Therefore, before introducing a road pricing policy, it is necessary to develop public transport system, especially rail transit.     

Investigating the learning effects of route guidance and traffic advisories on route choice behavior

This paper describes a study to investigate the effects of route guidance and traffic advisories on driver's route choice behavior. The study is a two-factor experiment with repeated measures on one factor where the between-subjects factor is the type of traveler information provided and the repeated, within-subjects factor is trips made between a specified origin and destination. Participants were recruited and randomly assigned to one of four groups: group 1 having only a basic map of the network; group 2 having access only to route guidance, group 3 having access to traffic advisory information, and group 4 having access to both route guidance and traffic advisory information. Each participant completed 15 trips between a specified origin-destination pair on a hypothetical network. The results of this study indicate that there may be significant short-term advantages to providing in-vehicle routing and navigation information to unfamiliar drivers. However, the results also indicate that the format and amount of information provided may not be significant as the benefits to having route guidance diminish when drivers become more familiar with the travel network.     

A behavioural choice model for the evaluation of railway supply and pricing policies

This paper presents a choice model that can be used to simulate the impacts of variations in medium–long distance railway service characteristics (e.g. timetable, travel time, prices) on user choice of service type, run and class. The model, based on a nested-logit service/run/class/access–egress mode choice model, explicitly takes into account the desired departure (arrival) time and related early/late schedule delay penalties.     

Boundedly rational route choice behavior: A review of models and methodologies

Perfect rationality (PR) has been widely used in modeling travel behavior. As opposed to PR, bounded rationality (BR) has recently regained researchers’ attention since it was first introduced into transportation science in the 1980s due to its power in more realistic travel behavior modeling and prediction. This paper provides a comprehensive survey on the models of BR route choice behavior, aiming to identify current research gaps and provide directions for future research. Despite a small but growing body of studies on employing bounded rationality principle, BR route choice behavior remains understudied due to the following reasons: (a) The existence of BR thresholds leads to mathematically intractable properties of equilibria; (b) BR parameters are usually latent and difficult to identify and estimate; and (c) BR is associated with human being’s cognitive process and is challenging to model. Accordingly, we will review how existing literature addresses the aforementioned challenges in substantive and procedural bounded rationality models. Substantive bounded rationality models focus on choice outcomes while procedural bounded rationality models focus on the empirical studies of choice processes. Bounded rationality models in each category can be further divided based on whether time dimension is included. Accordingly, static and dynamic traffic assignment are introduced in substantive bounded rationality while two-stage cognitive models and day-to-day learning models in procedural bounded rationality are discussed. The methodologies employed in substantive bounded rationality include game theory and interactive congestion game, while those in procedural bounded rationality mainly adopt random utility and non- or semi-compensatory models. A comparison of all existing methodologies are given and bounded rationality models’ scope and boundaries in terms of predictability, transferability, tractability, and scalability are discussed. Finally existing research gaps are presented and several promising future research directions are given.     

The impacts of time of day pricing on the behavior of freight carriers in a congested urban area: Implications to road pricing

This paper describes the key findings from a major research project aimed at assessing the impacts of the Port Authority of New York and New Jersey’s time of day pricing initiative on the behavior of commercial carriers. The paper, believed by the authors to be the first comprehensive study on the subject, highlights key implications for road pricing policy.One of the most interesting findings is that carriers respond to time of day pricing by implementing multi-dimensional responses involving Productivity increases, Cost transfers, and Change in facility usage. This implies a more nuanced response than suggested by micro-economic theory, which would only predict a change in facility usage. In fact, no carrier was found to have responded by implementing only changes in facility usage, which leads to the authors to believe that this is a last resort alternative.In terms of numerical importance, three combinations of strategy groups represent almost 90% of the cases: Productivity increases (42.79%), followed by Changes in facility usage and Cost transfers (27.60%) and Productivity increases and Changes in facility usage and Cost transfers (19.32%). The fact that some of these responses impact only the carrier (i.e., Productivity increases) while others mostly impact the receivers (Changes in facility usage and Cost transfers) lead the authors to believe that the nature of the response is determined by the balance of power between carriers and receivers. If carriers dominate the relationship, then it is likely that policies that mostly impact receivers are implemented; otherwise, the carriers have no choice but implementing strategies that help them cope with the impacts of pricing without impacting their customers, i.e., productivity increases. In this context, the authors’ conjecture is that carriers consider changes in facility usage to be a very disruptive alternative that forces them—and more importantly their customers—to alter their shipping/delivery patterns. It should be pointed out that, although carriers stand to benefit from working during the off-peak hours, they could only do so if their customers are willing to work during the off-peak hours.The data indicate that 36 carriers (20.2%) changed behavior because of the time of day pricing initiative. This number includes 17 carriers (9.0%) that reacted by increasing shipping charges to receivers, which illustrates the need to find out more about how receivers reacted to the time of day pricing initiative. If the carriers that only increased shipment charges are excluded, 15.3% of carriers changed behavior because of time of day pricing.     

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.     

Evaluating the benefits of a combined route guidance and road pricing system in a traffic network with recurrent congestion

When drivers do not have complete information on road travel time and thus choose their routes in a stochastic manner or based on their previous experience, separate implementations of either route guidance or road pricing cannot drive a stochastic network flow pattern towards a system optimum in a Wardropian sense. It is thus of interest to consider a combined route guidance and road pricing system. A road guidance system could reduce drivers' uncertainty of travel time through provision of traffic information. A driver who is equipped with a guidance system could be assumed to receive complete information, and hence be able to find the minimum travel time routes in a user-optimal manner, while marginal-cost road pricing could drive a user-optimal flow pattern toward a system optimum. Therefore, a joint implementation of route guidance and road pricing in a network with recurrent congestion could drive a stochastic network flow pattern towards a system optimum, and thus achieve a higher reduction in system travel time. In this paper the interaction between route guidance and road pricing is modeled and the potential benefit of their joint implementation is evaluated based on a mixed equilibrium traffic assignment model. The private and system benefits under marginal-cost pricing and varied levels of market penetration of the information systems are investigated with a small and a large example. It is concluded that the two technologies complement each other and that their joint implementation can reduce travel time more efficiently in a network with recurrent congestion.     

Preliminary analysis on dynamic route choice behavior: Using probe‐vehicle data

Dynamic traffic assignment models have been attracting increasing attention with the progress of traffic management policies based on information technology. These dynamic estimation tools, however, just apply static route choice models either at only origin node or at every arrival node. This paper aims at providing some knowledge on drivers' dynamic route choice behavior using probe‐vehicle data. The results of analyses show that route choice behavior relates to the distance from driver's position to the destination and that dynamic route choice behavior is modeled better by considering decision process during the trip.     

On dynamics of traffic queues in a road network with route choice based on real time traffic information

Introducing real time traffic information into transportation network makes it necessary to consider development of queues and traffic flows as a dynamic process. This paper initiates a theoretical study of conditions under which this process is stable. A model is presented that describes within-one-day development of queues when drivers affected by real-time traffic information choose their paths en route. The model is reduced to a system of differential equations with delay. Equilibrium points of the system correspond to constant queue lengths. Stability of the system is investigated using characteristic values of the linearised minimal face flow. A traffic network example illustrating the method is provided.     

Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model

Social interaction is increasingly recognized as an important factor that influences travelers’ behaviors. It remains challenging to incorporate its effect into travel choice behaviors, although there has been some research into this area. Considering random interaction among travelers, we model travelers’ day-to-day route choice under the uncertain traffic condition. We further explore the evolution of network flow based on the individual-level route choice model, though that travelers are heterogeneous in decision-making under the random-interaction scheme. We analyze and prove the existence of equilibrium and the stability of equilibrium. We also analyzed and described the specific properties of the network flow evolution and travelers’ behaviors. Two interesting phenomena are found in this study. First, the number of travelers that an individual interacts with can affect his route choice strategy. However, the interaction count exerts no influence on the evolution of network flow at the aggregate-level. Second, when the network flow reaches equilibrium, the route choice strategy at the individual-level is not necessarily invariable. Finally, two networks are used as numerical examples to show model properties and to demonstrate the two study phenomena. This study improves the understanding of travelers’ route choice dynamics and informs how the network flow evolves under the influence of social interaction.