Driver Route Choice Behavior Research Articles

Route choice model using angular indicators

The geometry of the road network significantly affects drivers’ route choice behavior. Among indicators of road network geometry, the lengths of links and connection relations have been widely focused on. On the other hand, indicators related to angles have not received much attention so far. The directional change between neighboring links may affect the route choice behavior among the angular indicators. For example, a route that is similar to a U-turn with a large angle right/left turn is less likely to be desired by drivers. On the other hand, routes that require large detours to reduce the directional change and increase the travel distance are also undesirable. The importance of the angular indicator in route choice behavior can be measured by determining the effect of the directional change on drivers’ route choices. This research examines how directional changes at intersections within the road network affect driver route choice behavior. Initially, we develop a discrete choice model that incorporates both directional changes and travel distance as components within the utility function. Subsequently, we estimate the model's parameters using actual route choice data to ascertain the influence of angular indicators on route selection behavior. In the case study, we observe that directional changes significantly affect route choice behavior, with a single left or right turn equivalent to an additional travel distance of approximately 1 kilometer.

Estimating Route Choice Characteristics of Truck Drivers from Sparse Automated Vehicle Identification Data through Data Fusion and Bi-Objective Optimization

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

Impacts of variable message signs on en-route route choice behavior

Abstract Background Variable message signs (VMS) have been widely applied to alleviate traffic congestion by providing spatiotemporal information related to traffic and network conditions to drivers. There is a debate on whether the VMS information would lead to the less-alleviation of congestion or congestion transfer to the adjacent roads in the different traffic congestion situations due to the inappropriate VMS information. Additionally, it is still unknown how to find a practically applicable method to design the most effective VMS information for drivers. Objective In order to design and display the appropriate VMS information for drivers under different traffic conditions, this paper aims to identify the effects of VMS on individuals’ route choice behavior by exploring the different effects of VMS on the several separate groups. Drivers in these groups are homogenous in gender, driving experience, education level, occupation, and monthly income. Methods In the hypothetical scenario, a stated preference (SP) questionnaire was conducted to build the dataset consisting of 3462 samples from local private and taxi drivers. The decision tree via the chi-squared automatic interactive detector (CHAID) algorithm is utilized to split drivers into several groups, and further two fixed-parameter logistic regression models considering intra-heterogeneity between drivers (i.e., considering and not considering systematic taste variations), and random-parameter logistic regression models are employed to analyze the factors affecting driver route choice behavior of each group. Results Five groups with different driver characteristics are identified. Besides, the modeling results indicate that the effect of the VMS information on individual route choice behavior is heterogeneous. These findings include: (i) random-parameter logistic regression models have a significantly improved goodness-of-fit because of the lower AIC and BIC; (ii) compared with the stated cause information on road construction and congestion, the accident information had a larger effect on driver route choice behaviors; (iii) the route choice behaviors of drivers with more than 6-year driving experience are affected by the VMS information on road constructions, accidents, and the quantitative delay time; (V) the route choice behaviors of less-experienced female drivers were affected by display formats and display frequencies of VMS information. Conclusions The findings from this study can assist traffic authorities in designing the most appropriate VMS for different traffic congestions based on driver characteristic distribution and the road capacity for improving the practicability of VMS information and further provide the theoretical evidence for the design of in-vehicle personalized information service systems.

Frequentist and Bayesian Approaches for Understanding Route Choice of Drivers under Stop-and-Go Traffic

Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large samples. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especially when the sample size is small. Thus, this paper develops and compares a multinomial logit (frequentist) and a Naïve Bayes (Bayesian) model on a mid-sized dataset of size around 100 participants which was obtained from a driving simulator experiment to understand driver’s route choice under stop-and-go traffic. The results show that the prediction power of the Naïve Bayes model is much higher than the multinomial logit model (MNL). The Naïve Bayes model is also found to perform better than machine learning algorithms like the decision tree model. The findings from this study will be useful to researchers and practitioners as they should test both the approaches and select the appropriate model, particularly in the case of seemingly large datasets.

Empirical Study of Effect of Dynamic Travel Time Information on Driver Route Choice Behavior.

The objective of this paper is to study the effect of travel time information on day-to-day driver route choice behavior. A real-world experimental study is designed to have participants repeatedly choose between two alternative routes for five origin-destination pairs over multiple days after providing them with dynamically updated travel time information (average travel time and travel time variability). The results demonstrate that historical travel time information enhances behavioral rationality by 10% on average and reduces inertial tendencies to increase risk seeking in the gain domain. Furthermore, expected travel time information is demonstrated to be more effective than travel time variability information in enhancing rational behavior when drivers have limited experiences. After drivers gain sufficient knowledge of routes, however, the difference in behavior associated with the two information types becomes insignificant. The results also demonstrate that, when drivers lack experience, the faster less reliable route is more attractive than the slower more reliable route. However, with cumulative experiences, drivers become more willing to take the more reliable route given that they are reluctant to become risk seekers once experience is gained. Furthermore, the effect of information on driver behavior differs significantly by participant and trip, which is, to a large extent, dependent on personal traits and trip characteristics.

A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles

Battery Electric Vehicles (BEVs) play an important role in the needed transition away from fossil fuels and Internal Combustion Engine Vehicles (ICEVs). Although transport planning models and routing problem solutions exist for BEVs, the assumption that BEV drivers search for the shortest path while constraining energy consumption does not have any empirical basis. This study presents a study of actual route choice behavior of drivers from 107 Danish households participating in a large-scale experiment with BEVs and at the same time driving their ICEVs. GPS traces from 8968 BEV and 6678 ICEV routes were map matched to a detailed road network to construct observed routes, and a route choice model was specified and estimated to capture behavioral differences related to the vehicle type. The results reveal that drivers had a higher sensitivity to travel time and trip length when driving BEVs, and to route directness after receiving the BEV, regardless of vehicle type. The results suggest the need to revise the assumptions of transport planning models and routing problems for BEVs in order not to fail to predict what drivers will do by ignoring differences and similarities related to vehicle type.

Integrated Empirical Analysis of the Effect of Variable Message Sign on Driver Route Choice Behavior

AbstractThis paper investigated the effect of a variable message sign (VMS) on drivers’ route choice behavior through a questionnaire survey and field-observed route choice data. A logistic model w...

A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation rate, path travel time, cumulative intersection delay, path distance, and path size. Specially, customer generation rate is defined as attraction strength based on historical pick-up records in the route, the intersection travel delay and path travel time are estimated based on large scaled taxi global positioning system (GPS) trajectories. In the experiment, the GPS data were collected from about 36000 taxi vehicles in Beijing at 30-s interval during six months. In the model application, an area of approximately 10 square kilometers in the center of Beijing is selected to demonstrate the effectiveness of the proposed model. The results indicated that the MPSL model could effectively analyze the route choice behavior in customer-searching process and express higher accuracy than traditional multinomial logit model and basic PSL model.

A data driven approach to assessing the reliability of using taxicab as probes for Real-Time route selections

Taxi service is one of the most important modes for urban transportation. In recent years, many taxi companies have been routinely collecting data to track the movement of each taxi for improving security, coordination, and service performance. This paper is intended to use the GPS vehicle positioning data to assess the route choice behavior of taxi drivers and explore if the routes selected by taxi drivers can be incorporated into a traveler information system. It is often perceived that taxi drivers have the ability to select quality routes assuming that: (1) they tend to be more knowledgeable about alternative routes and time-dependent traffic conditions than general public, including some publicly available route guidance systems due to the nature of their profession; and (2) they are typically more motivated to incorporate their knowledge about traffic conditions into their route choice decisions. An experimental study is conducted to examine the validity of these two assumptions. We have developed a framework that can effectively process the data into information about routes selected by taxi drivers and their associated travel times. The performance of the routes selected by taxi drivers is compared with the performance of those recommended by e-maps. Our results indicate that the routes selected by taxi drivers are generally more efficient than the routes recommended by some major e-maps, suggesting that taxi drivers are more active in selecting routes to avoid congestion.

Frequent versus occasional drivers: A hybrid route choice model

Previous route choice studies have mostly focused on the effect of observable factors, such as route attributes and socio-economic characteristics, on individuals’ decisions. However, route choice decisions might not be exclusively dependent on these observable variables, but also on latent variables, which cannot be directly observed and measured. Also, the latent behavioral heterogeneity among the population has mostly been ignored by assuming that all the individuals in the sample population have similar attitudes, perceptions, and lifestyles.In this paper, we present a comprehensive framework to explicitly incorporate latent behavioral constructs as well as segment heterogeneity based on a probabilistic segmentation of the population. We apply the proposed framework to compare the route choice behavior of frequent versus occasional drivers. An Integrated Choice and Latent Variable model is used to bring in the role of the underlying behavioral constructs, while a Latent Class model accounts for taste heterogeneity across the two segments of our sample population. An Extended Path-Size Logit model is adopted as the choice model component and a Metropolis-Hastings based algorithm is used to generate route alternatives. Data is collected through a web-based survey designed to collect behavioral data on drivers’ route choices, using psychometric indicators and behavioral questions on respondents’ perceptions and attitudes. Results confirm that the inclusion of latent variables and latent heterogeneity across population segments significantly improve the explanatory power of the choice model, and illustrate how the route choice behavior of frequent car users is different from that of occasional ones.

Characterizing multicity urban traffic conditions using crowdsourced data.

Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.

A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment

Drivers can move with the real-time traffic information under the connected vehicle environment. Meanwhile, individual driving behavior (e.g., route choice) can influence the traffic state of the connected vehicle. This study contributes to investigate the relationship between the performance of traffic system and the driver route choice behavior in the connected vehicle environment. Especially, the heterogeneity of drivers in attention to fuel consumption and travel time is considered. A dynamic process of commuter day-to-day route choice behavior under the connected vehicle environment was proposed, the driving experience and the real-time information that affect the driver route choice are taken into considered simultaneously. Using the multi-agent simulation approach, the microcosmic and macroscopic behaviors of driver-vehicle-unit agents were described. A series of simulation experiments were conducted under the different percentages of drivers who pay enough attention to fuel consumption, and then simulation results were compared. The simulation results demonstrated that the performance of traffic state is better when enough drivers put emphasis on fuel consumption, and the optimal percentage of these drivers is about 60%. It is indicated that the travel time is more sensitive to the change of traffic state than fuel consumption, and the traffic system with enough drivers who care the fuel consumption could bring the better performance. This study has a guidance function to the application and promotion of eco-driving for government to optimize the structure of different types of drivers in the connected vehicle environment.

Integration of a driving simulator and a traffic simulator case study: Exploring drivers' behavior in response to variable message signs

For the first time, a driving simulator has been integrated with a traffic simulator at the network level to allow subjects to drive in a fairly realistic environment with a realistic traffic flow and density. A 10mi2 (25km2) network was developed in a driving simulator and then exported to a traffic simulator. About 30 subjects drove the simulator under different traffic and driving conditions and variable message sign (VMS) information, both with and without integration. Route guidance was available for the subjects. The challenges of the integration process are explained and its advantages investigated. The study concluded that traffic density, VMS reliability and compliance behavior are higher when driving and traffic simulators are integrated. To find factors affecting route diversion, researchers applied a binary logistic regression model. The results indicated that the original chosen route, displayed VMS information, subjects' attitude toward VMS information helpfulness, and their level of exposure to VMS affect route diversion. In addition, a multinomial logistic regression model was employed to investigate important factors in route choice. The results revealed that there is a significant correlation with driver route choice behavior and their actual travel time, the need for GPS, VMS exposure and also the designed scenarios. It should be noted that the paper was peer-reviewed by TRB and presented at the TRB Annual Meeting, Washington, D.C., January 2016.

Routing aspects of electric vehicle drivers and their effects on network performance

This study investigates the routing aspects of battery electric vehicle (BEV) drivers and their effects on the overall traffic network performance. BEVs have unique characteristics such as range limitation, long battery recharging time, and recuperation of energy lost during the deceleration phase if equipped with regenerative braking system (RBS). In addition, the energy consumption rate per unit distance traveled is lower at moderate speed than at higher speed. This raises two interesting questions: (i) whether these characteristics of BEVs will lead to different route selection compared to conventional internal combustion engine vehicles (ICEVs), and (ii) whether such route selection implications of BEVs will affect the network performance. With the increasing market penetration of BEVs, these questions are becoming more important. This study formulates a multi-class dynamic user equilibrium (MCDUE) model to determine the equilibrium flows for mixed traffic consisting of BEVs and ICEVs. A simulation-based solution procedure is proposed for the MCDUE model. In the MCDUE model, BEVs select routes to minimize the generalized cost which includes route travel time, energy related costs and range anxiety cost, and ICEVs to minimize route travel time. Results from numerical experiments illustrate that BEV drivers select routes with lower speed to conserve and recuperate battery energy while ICEV drivers select shortest travel time routes. They also illustrate that the differences in route choice behavior of BEV and ICEV drivers can synergistically lead to reduction in total travel time and the network performance towards system optimum under certain conditions.

Statistical Investigations of Snowfall and Temperature Interaction with Passenger Car and Truck Traffic on Primary Highways in Canada

AbstractWinter weather conditions such as extremely cold temperatures, heavy snowfall, and high wind chills are common occurrences in Canada. Impacts of such adverse weather conditions on total highway traffic volume have been the subject of several research studies in the past. However, none of the past studies investigated thoroughly the impacts of severe cold and heavy snowfall on temporal and spatial variations of truck traffic on Canadian highways. Impacts of weather on route choice behavior of truck and passenger car drivers have also not been addressed in the past. This paper presents an in-depth analysis of the winter weather impacts on classified traffic volume in terms of passenger cars and trucks with considerations of highway types. This study is based on large traffic and weather data sets from weigh-in-motion sites and weather stations in Alberta, Canada. The data were collected from six sites located on two primary highways: Highway 2 and Highway 2A, in Alberta. Winter-weather traffic model...

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.

Modeling Driver Behavior in a road network with route choice based on real time traffic information

Intelligent Transportation Systems (ITS) applications require a thorough understanding of drivers' route choice behavior in a complex network under real-time information. The purpose of this paper is to describe and model driver route choice behavior in a road network based on real time traffic information at the disaggregate individual level and from a psychological decision-making process perspective. The framework of routing choice and driver dynamic route choice behavior model that uses concepts from Decision Field Theory (DFT) and Bayesian belief network (BBN) is proposed. A real-time planning algorithm for route choice processes is discussed in great detail. Using this algorithm, a driver develops his route dynamically until he reaches his destination. The simulation results show that the combination of DFT and BBN can effectively describe the driver's travel dynamics behavior . DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4466 Full Text: PDF

A study of driver's route choice behavior based on evolutionary game theory.

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent.

An Analysis of Driver's Choice based on Information of Traffic Accidents

To enhance the effectiveness of dynamic information systems and ensure its applicability as a new means to control the influence of non-recurrent events including traffic accident, it is necessary to investigate the software aspects of the information system that include contents, timing, accuracy of information and so on. The following research subjects are treated in this study. 1) The characteristics of both actual and potential users of information on traffic accidents are investigated to discuss the future direction of the information system. 2) In order to analyze the relation between the way to provide drivers with information and driver's decision-making on route, driver's route choice behavior is investigated and analyzed by the stated preference survey method.

Taxi Driver's Route Choice Behavior Analysis Based on Floating Car Data

Taxi drivers are viewed having more driving experience, being more familiar with road traffic condition, and in turn having more rational route choice behaviors than ordinary drivers. Using floating car data (FCD) of Beijing taxi in 2010, this study discusses the influence of road network conditions and traffic status to taxi drivers route choice behaviors. First, trip information is extracted from FCD using trip-identification method; Second, map matching and K-shortest paths are used to construct the trajectories and the sets of alternate routes, and route similarity evaluation is conducted to build the sample data of route choice behavior analysis; Finally, route choice model for taxi drivers based on Multinomial Logit (MNL) Model is estimated. The result shows that taxi drivers tend to choose the route which has faster driving speed, less frequency of left turns, more proportion of express way and less proportion of minor road, and increasing a left-turn or decreasing travel speed by 2.12km/h has the same effect on route choice utility. This study is expected to be helpful to establish map-matching algorithm of FCD, route guidance scheme and traffic assignment model.