Driver Route Research Articles

A Hybrid Tree Approach to Modeling Alternate Route Choice Behavior With Online Information

One popular intelligent transportation systems (ITS) element that has been deployed in various areas of the United States is variable-message signs (VMSs). Such signs are part of advanced traveler information systems, and they can help drivers make better en route decisions regarding their trips. Unlike most other ITS elements (e.g., ramp metering, advanced signals), VMSs are not supported by mandatory regulations. Consequently, the expected benefit from implementing VMSs largely depends on the response of drivers. This study explored the factors affecting alternative route choices of car drivers with VMSs on the interstate highway system. This study adopted a method called LOTUS to analyze driver-compliance behavior with VMSs. Driver behavior under information provision through VMS data has been collected using the stated-preference approach. The study showed that travel-time saving is not the single dominant factor for driver route choice under information provision. This article explores variables that are associated with driver compliance.

Design and implementation of a portable electronic system for vehicle–driver–route activity measurement

This paper presents the autonomous, non-intrusive and universal electronic equipment PGAMS: Portable Global Activity Measurement System, which is able to simultaneously measure and synchronously register the treble driver–vehicle–route activity. It allows one to evaluate the direct effect that real road traffic conditions have on environmental pollution. Among the variables to be simultaneously measured are: (a) from vehicle activity: regime engine and temperature, fuel consumption, global position, instantaneous and average speed and acceleration, frontal and lateral inclination; (b) related to route features: weather conditions, slope and camber of the road, traffic density; and (c) derived from driver activity: clutch, throttle and brake pedal actions. The designed prototype can be on boarded in conventional or industrial vehicles, working in both urban and inter-urban conditions. The paper describes the integration with pollutant emissions meters. Besides, it includes experimental results carried out in real traffic conditions relating vehicle–driver–route activity with gasses emissions.

Calibration Issues for Multistate Model of Travel Time Reliability

Travel time reliability has received much attention from researchers and practitioners because it not only affects driver route choice behavior but also is used in the assessment of transportation system performance. Current state-of-the-art travel time reliability research assumes that roadway travel times follow a unimodal distribution. Specifically, a Weibull, exponential, lognormal, or normal distribution is used within the context of travel time reliability modeling. However, field observations demonstrate that roadway travel times are multimodal, especially during peak periods. This paper demonstrates that the multimodes observed in field data are a result of temporal variations in travel times both between and within days. This study evaluates the potential for generating multimodal travel times by using INTEGRATION software and investigates the underlying traffic states that result in these multimodes. The study validates the two-state model proposed in an earlier study and demonstrates the robustness of model parameter estimates under varying stochastic traffic congestion levels and sampling techniques.

Where will they turn: predicting turn proportions at intersections

Predicting a driver's route would be useful for warning a driver of upcoming road hazards, informing about traffic situations, and serving relevant advertising. There are many clues to a driver's future route, including their past behavior and likely destination. Another clue is the driver's turn choices at a sequence of intersections. Strung together, the turn choices form a route. This paper develops a basic algorithm, and variations, to predict the aggregate turn behavior of drivers at intersections. Given an intersection with a few different turn options, including the option to continue straight ahead, our goal is to infer the proportion of drivers who will take each option. For ground truth, we use raw turn counts gathered for government traffic studies by our local municipality at 40 different intersections. Our basic turn prediction algorithm is based on the assumption that drivers tend to choose roads that offer them more destination options. This matches our intuition that turning onto a short, dead-end road is relatively rare compared to turning onto a highway "on" ramp. The best performing algorithm predicts turn proportions with a median error of 0.142.

Study on Route Choice Behavior by Considering Driver's Experience and Provision of Traffic Information

At present, providing real time traffic information to the drivers became possible by the development of ITS (Intelligent Transportation System). However at present route choice model applying traffic simulations used by the transport planner is not considering the influence of provision of real time traffic information. Therefore, this study has an aim to propose driver's route choice model which considers the driver's experience and provision of real time traffic information and to forecast the impact to traffic condition. The authors applied the driver's behavior logit-model to the micro traffic simulation system tiss-NET, and conducted virtual experiment.

Questionnaire surveys to evaluate user response to variable message signs in an urban network

The use of variable message signs (VMS) to provide traffic information is widespread, but success depends upon behavioural response to the sign messages. The research described here focuses on user acceptance of VMS located in an urban road network (Southampton, UK), and investigates the public's perceived effectiveness and usefulness of these signs through the use of revealed preference questionnaire surveys and travel diaries. Three hundred and sixty-five commuters regularly driving into Southampton from the outskirts of the city completed a travel diary for a 5-day period as well as a general questionnaire, and 660 infrequent travellers to Southampton completed the general questionnaire. The study showed that it is difficult to capture a meaningful sample size of respondents passing an `active' VMS in a real-life incident scenario. Less than 1% of the commuter sample stated that they had diverted to an alternative route during the travel diary week as a result of VMS information, although this did correspond to 53% of those 45 drivers originally intending to travel past the incident location. The results showed that the VMS messages were well-understood and legible, and also indicated that a default VMS message reporting no problems in the network can indirectly affect a driver's route choice.

A static empty equipment allocation model for long-haul networks with constrained crew routes

A realistic and efficient tactical model is developed that is able to optimize equipment and crew movements in long-haul trucking networks so that drivers are able to return home within a reasonable amount of time. A unique feature of the model is that driver, tractor, and trailer routes are simultaneously optimized. An underlying assumption is that routes remain in place for extended periods; though the trailers, tractors, and drivers may flow across these routes at variable rates. We formulate a static linear programming model for this tactical problem, and propose a column generation algorithm. The algorithm is tested with real data from a less-than-truckload carrier and randomly generated test data. Networks with up to 40 nodes (including the 30-node real-life example) are successfully optimized. For our sample problems, we found that static routes produced solutions within an average 0.22% of the optimum under dynamic conditions, provided that variable flows are permitted across the static routes.

An optimization approach for planning daily drayage operations

Daily drayage operations involve moving loaded or empty equipment between customer locations and rail ramps. Our goal is to minimize the cost of daily drayage operations in a region on a given day. Drayage orders are generally pickup and delivery requests with time windows. The repositioning of empty equipment may also be required in order to facilitate loaded movements. The drayage orders are satisfied by a heterogeneous fleet of drivers. Driver routes must satisfy various operational constraints. We present an optimization methodology for finding cost-effective schedules for regional daily drayage operations. The core of the formulation is a set partitioning model whose columns represent routes. Routes are added to the formulation by column generation. We present numerical results for real-world data which demonstrate that our methodology produces low cost solutions in a reasonably short time.

Development of Life-Cycle Cost Evaluation Model for Pavements Considering Drivers' Route Choices

This study proposes a model for evaluating life-cycle cost (LCC) for pavements considering both driver's route choice behavior and repair. It is assumed that drivers change their route choices because of both the change in driving cost brought about by the deterioration of surface conditions on pavements and the change in traffic capacities due to repair work on pavements. The LCC minimization problem is formulated as the implicit program, in which LCC is minimized subject to the probit-based stochastic user equilibrium (PSUE), which expresses drivers' route choices. The solution algorithm for the LCC minimization problem developed by applying the sensitivity analysis technique for the PSUE is also presented. Last, the simple numerical results obtained by using the test network are shown.

Development of Life-Cycle Cost Evaluation Model for Pavements considering Drivers’ Route Choices

This study proposes a model for evaluating life-cycle cost (LCC) for pavements considering both driver's route choice behavior and repair. It is assumed that drivers change their route choices because of both the change in driving cost brought about by the deterioration of surface conditions on pavements and the change in traffic capacities due to repair work on pavements. The LCC minimization problem is formulated as the implicit program, in which LCC is minimized subject to the probit-based stochastic user equilibrium (PSUE), which expresses drivers’ route choices. The solution algorithm for the LCC minimization problem developed by applying the sensitivity analysis technique for the PSUE is also presented. Last, the simple numerical results obtained by using the test network are shown.

Normalization methods on backpropagation for the estimation of driver's route choice

The artificial neural network has recently been applied in many areas including transport engineering and planning. Even though its successful application for wide transportation areas, there are some major issues to be considered before using the neural network models, such as the network topology, learning parameter, and normalization methods for the input vectors. In this research, se veral normalization methods for input vectors were studied and the experimental results showed that the performance of the drier's ro ute choice model using the neural networks was dependent on the normalization methods. For the estimation of driver's route choice, the best normalization method in the Backpropagation neural network model was suggested in this study.

A Hybrid Model for Driver Route Choice Incorporating En-Route Attributes and Real-Time Information Effects

The en-route driver behavior problem under information provision is characterized by subjective and linguistic variables, in addition to situational factors. Fuzzy modeling provides a robust mechanism to capture subjectivity and/or the linguistic nature of the causal variables. This motivates the development of a hybrid en-route route choice model that combines quantitative and fuzzy variables to more robustly predict driver routing decisions under information provision. Simulation experiments are conducted to analyze the ability of the hybrid model to capture en-route driver behavior effects in the within-day and day-to-day contexts.

THE IMPACT OF TRAFFIC INFORMATION ON DRIVERS' ROUTE CHOICE-USING COMPETENCE SETS ANALYSIS

Generally speaking, drivers' route choice is a fuzzy problem. However, if drivers' habitual domain becomes stable and without significant stimuli, route choice becomes a routine problem. Route choice can become fuzzy again if drivers perceive information stimuli. Intuitively, traffic information should help drivers to reach destination in an efficient way, but the abundant and complex information could overwhelm the drivers. In this work, a different and novel approach to complement the driver route choice decision models is exploited. The concept of habitual domains and competence sets proposed by Yu in 1980 is applied to the route choice problem. The effect of traffic information on route choice is isolated to analysis the behavior of route choice decision. Performance indexes of route choice decision are developed to help the drivers or the traffic information providers in expanding their competence sets to fully address the needs of route choice decision.

Adaptability of a Hybrid Route Choice Model to Incorporating Driver Behavior Dynamics Under Information Provision

This paper proposes a seamless framework to incorporate the day-to-day and within-day dynamics of driver route choice decisions under real-time information provision by adapting a hybrid probabilistic-possibilistic model previously developed by the authors. The day-to-day dynamics are captured through the update of driver perception and route choice rules based on the current day's experience. The within-day dynamics are captured through the en-route adjustment of the weights of the driver route choice rules in response to situational factors. Experiments are conducted to analyze the model's ability to capture driver behavior dynamics, and the associated prediction accuracy. The results suggest that the framework can reflect the evolution of driver route choice behavior over time, and adapt to the within-day variability in ambient driving conditions. This is illustrated by its ability to capture phenomena such as inertia, compliance, delusion, freezing, and perception update under information provision, in addition to the effects of familiarity and route complexity. In the within-day context, the results highlight the sensitivity to the situational factors unfolding in real-time. The results also illustrate the better prediction power of the hybrid model compared to that of a traditional multinomial probit model; however, this gap reduces with increasing heterogeneity in driver behavioral class fractions. Elsewhere, the authors show that the proposed framework can be used to predict the ambient driver class fractions, thereby addressing a key deployment limitation of existing dynamic network models for real-time traffic control through route guidance.

An Analysis of Effects of Traffic Information for Expressway with Multiple Routes Using SP Survey

With expansions of highway network, more attention has been paid to providing drivers with dynamic information from the viewpoints of traffic control schemes. This study aims at discussing the suitable way to provide drivers including elderly ones with traffic information and analyzing driver's route choice under provision of improved traffic information. This study conducted the questionnaire survey composed of the questions related to driver's attitude toward information provided and the stated preference survey about route choice under provision of jam information. The estimated parameters of route choice models suggest that information on short-term trend of jam might influence driver's decision-making.

Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment

If real-time driver en route guidance advice does not meet driver preferences (e.g., preference for taking the freeway) or the advice is not correct, drivers are very likely to ignore the information, and the guidance system becomes ineffective in their route choice no matter how advanced the system. There is a need to investigate the factors affecting driver compliance with en route guidance advice. A travel simulation experiment was used to investigate significant factors affecting driver route choice behavior. A linear mixed model was developed for describing the factors affecting driver compliance with guidance advice using the compliance rate over several simulated trips as a dependent variable. The issue of repeated observations is addressed. The system accuracy and subjects’ learning experience in their spatial experience at the same intersection and temporal experience in the same day are also taken into account. The model results show that significant factors are involved: freeway advice, turning advice, congestion occurrence, incident occurrence, subjects’ spatial experience, subjects’ temporal experience, and subjects’ education level; there are several important interactions as well.

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.

Analysis of heteroscedasticity and correlation of repeated observations in Stated Preference(SP) data

The repeated measurement problems on Stated Preference (SP) data can be defined as the treatment of the combination of different variances (heteroscedasticity) and correlation of repeated observations from each individual. However, the repeated measurement problem has been approachedonly either as upward biased t-ratioor as correlation of disturbances according to repeated observations. Purposes of this paper are to consider the correlation in repeated observations from each individual and the heteroscedasticity of repeated observations from SP data and to suggest practical methods against the repeated measurement problem. This paper compares results of these approaches with those of the existing standard methods suggested. To identify the heteroscedasticity of the repeated observations, standard multiomial logit models with different number observations are estimated and then heteroscedasticity tests are accomplished. Correlation in repeated observations from each individual is considered by incorporating a stochastic individual heterogeneity variable. We specify this individual heterogeneity variable to vary following a normal distribution and the random variation effect is forced to be same within individual, but to be normally distributed across people. The SP data from a survey of drivers route choice in response to traffic information are used in this application. This research will contribute to identification of the errors in SP data and will high light the biased parameter estimates in absolute coefficients or coefficients ratios when the repeated SP observations are applied.

Data-Consistent Fuzzy Approach for Online Driver Behavior Under Information Provision

A fuzzy approach to predict online driver routing decisions under information provision is proposed. The problem is characterized by subjectively interpreted and linguistic data, limited data on qualitative variables, and the need for computational efficiency to enable real-time deployment in a traffic control framework. The appropriateness of a fuzzy model to address these characteristics is highlighted. The proposed model enables theoretical consistency with the underlying behavioral mechanisms revealed through the available data. This is done by transforming the probabilities obtained from the data to possibilities, and then constructing membership functions. An S-shaped curve is proposed as a more robust behavioral indicator than the often-used triangular and trapezoidal shapes for these functions, while retaining their analytical tractability, computational efficiency, and ease of construction. In general, the approach suggests membership functions that are consistent with actual data. The model is analyzed using on-site stated preference data on variable message sign driver route diversion attitudes and is compared with a probabilistic discrete choice model for the same data. The results indicate that the fuzzy model performs more robustly for these data. When its computational efficiency and modularity are factored in, it may represent a better alternative to model online driver routing behavior in the context of real-time deployment.

Control of vehicle dispatching on a cyclic route serving trucking terminals

In the less-than-truckload industry, it is commonplace to designate specific routes for tractors and drivers that remain in effect over extended time periods, lasting weeks or months. On a day-to-day basis, however, the number of tractors traveling on a route can vary in relation to demand. In this paper, we study routes that consist of two or more segments and two or more stops that define a cyclic tour for a tractor/driver team. At each stop, the driver/tractor can drop off one or more trailers, and pick up one or more trailers. The paper develops control rules based on predicted route productivity to determine when to release a tractor. The approximation is accurate when loops are relatively short, when the number of tractors traveling on a loop at any time is small, or when the productivity achieved is large. In other instances, the approximation can be used to guide the release of tractors, following a policy that adapts to calculated biases.