Evaluating the Safety Implications and Benefits of an In-Vehicle Data Recorder to Young Drivers

Young drivers in Israel, as in other parts of the world, are involved in car crashes more than any other age group. Green Light for Life is a new program that seeks to improve the quality of the experience of young drivers during the mandatory accompanied driving period. As part of the efforts to evaluate the effectiveness of this program a novel experiment, which uses information gathered from an in-vehicle data recorder (IVDR) is conducted. The DriveDiagnostics IVDR system, which is used in this study, can identify over 20 different maneuver types in raw measurements and use this information to indicate overall trip safety. Drivers receive feedback through various summary reports, real-time text messages or an in-vehicle display unit. Preliminary validation tests with the system demonstrate promising potential. In the experiment, the DriveDiagnostics system is installed in the primary vehicle driven by the young driver in 120 families. The experiment is designed to test the impact on driving behavior of participation in the program and the type of feedback drivers receive from the system. The data collection part of the experiment is scheduled to run for 8 months for each family

Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority

In traffic networks, appropriately determining the traffic signal plan of each intersection is a ünecessary condition for a reasonable level of service. This paper presents the development of a new system for automatically designing optimal actuated traffic signal plans with transit signal priority. It uses an optimization algorithm combined with a mesoscopic traffic simulation model to design and evaluate optimal traffic signal plans for each intersection in the traffic network, therefore reducing the need for human intervention in the design process. The proposed method can simultaneously determine the optimal logical structure, priority strategies, timing parameters, phase composition and sequence, and detector placements. The integrated system was tested by a real-world isolated intersection in Haifa city. The results demonstrated that this approach has the potential to efficiently design signal plans without human intervention, which can minimize time, cost, and design effort. It can also help uncover problems in the design that may otherwise not be detected

A Gradient Projection Algorithm for Side-constrained Traffic Assignment

Standard static traffic assignment models do not take into account the direct effects of capacities on network flows. Separable link performance functions cannot represent bottleneck and intersection delays, and thus might load links with traffic volumes, which far exceed their capacity. This work focuses on the side-constrained traffic assignment problem (SCTAP), which incorporates explicit capacity constraints into the traffic assignment framework to create a model that deals with capacities and queues. Assigned volumes are bounded by capacities, and queues are formed when capacity is reached. Delay values at these queues are closely related to Lagrange multipliers values, which are readily found in the solution. The equilibrium state is defined by total path travel times, which combine link travel times and delays at bottlenecks and intersections for which explicit capacity constraints have been introduced. This paper presents a new solution procedure for the SCTAP based on the inner penalty function method combined with a path-based adaptation of the gradient projection algorithm. This procedure finds a solution at the path level as well as at the link level. All intermediate solutions produced by the algorithm are strictly feasible. The procedure used to ensure that side-constraints are not violated is efficient since it is only performed on constrained links that belong to the shortest path

The role of personality factors in repeated route choice behavior: behavioral economics perspective

This paper is based on an in-laboratory experiment and aims to explore the impact of various personality factors on route-choice behavior in the presence of partial pre-trip travel time information. Specifically, these factors are geographic ability and sensation seeking characteristics. The results show that while the variables related to perceived and realized travel times are important, the personality factors are also significant. Drivers with lower geographic abilities tended to use the main route more often and to switch their routes less often, compared to those with higher capabilities. Drivers who scored higher on sensation seeking tended to switch their routes more frequently, compared to other drivers

Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models

Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis

Modeling the Behavior of Novice Young Drivers Using Data from In- Vehicle Data Recorders

Novice young drivers suffer from increased crash risk that translates into over-representation in road injuries. A better understanding of the driving behavior of novice young drivers and of their determinants is needed to tackle this problem. To this extent, this study analyzes the behavior of novice young drivers within a Graduated Driver Licensing (GDL) program. Data on driving behavior of novice drivers and their parents is collected using in-vehicle data recorders, which calculate compound risk indices as measures of the risk taking behavior of the various drivers. Data is used to estimate a negative binomial model to identify the major factors that affect the driving behavior of the young drivers. Estimation results suggest that the risk taking behavior of young drivers is influenced by that of their parents and decreases with higher levels of supervised driving and stricter monitoring by the parents

Integrating Kinematic- and Vision-Based Information to Better Understand Driving Behaviour

This study explored the use of two types of advanced driver assistance systems (ADAS) as tools for observing driving behavior. The first was a kinematic-based ADAS that uses speed and acceleration data to detect driving events such as hard braking, speeding and sharp turning. The second was a visionbased ADAS that uses video data to provide lane departure warnings (LDW), headway warnings (HW) and forward collision warnings (FCW). Data was collected for more than 4,500 trips and 2,200 driving hours during a period of 70 days. The sample consisted of 10 drivers that used both types of ADAS simultaneously. The information collected also included more than 17,000 records of various types of driving events. First, the events rates were estimated by the Poisson and the Poisson-lognormal models. Then, Pearson correlation and factor analysis were implemented to study the relationships among the events and to evaluate whether different types of events converged to describe the same behaviors. Significant correlations were observed between the braking and turning kinematic-based events and the FCW vision-based event, which converged under the same factor. High rates of these events may indicate that the person is driving in an urban style. The LDW, HW and speeding events converged to the second factor, which is more relevant in inter-urban areas. These findings, although based on a small-scale study, point to a potential for the use of commercial ADAS for driving behavior analysis. The integration of kinematic-based and vision-based information can provide deeper understanding of the measured behavior

Car following with an inertia-oriented driving technique: A driving simulator experiment

For many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course

The Potential for IVDR Feedback and Parental Guidance to Improve Novice Young Drivers’ Behavior

Young male drivers are well known for their increased involvement in road crashes when moving to the independent driving phase. This study examines the potential of IVDR (In-Vehicle Data Recorder) systems, which provide feedback on driving performances, and parental monitoring to restrain young male drivers’ aggressive driving behavior. The IVDR system was installed in the family car of young drivers for a period of 12 months, starting in the accompanied driving phase and continuing to the first nine months of independent driving. The system documents events based on measurements of extreme G-forces in the vehicles. 242 families of young male drivers participated in the study. They were randomly allocated into 4 groups: (1) FFNG- Family Feedback No Guidance- all members of the family were exposed to feedback on their own driving behavior and that of the other family members; (2) FFPG- Family Feedback Parental Guidance - similar to the previous group with the addition of personal guidance given to parents on ways to enhance their involvement and monitoring of their sons’ driving; (3) IFNG- Individual Feedback No Guidance- each driver received feedback only on his own driving behavior; (4) CNTL- a control group that received no feedback or parental guidance. The collected data from the IVDR was analyzed and the results indicate substantial benefits to drivers in the FFPG group in which parents received personal guidance to enhance their parental involvement and feedback on their son’s driving behavior, compared to the CNTL group which did not receive any feedback

Modeling reaction time within a traffic simulation model

Human reaction time has a substantial effect on modeling of human behavior at a microscopic level. Drivers and pedestrian do not react to an event instantaneously; rather, they take time to perceive the event, process the information, decide on a response and finally enact their decision. All these processes introduce delay. As human movement is simulated at increasingly fine-grained resolutions, it becomes critical to consider the delay due to reaction time if one is to achieve accurate results. Most existing simulators over-simplify the reaction time implementation to reduce computational overhead and memory requirements. In this paper, we detail the framework which we are developing within the SimMobility Short Term Simulator (a microscopic traffic simulator), which is capable of explicitly modeling reaction time for each person in a detailed, flexible manner. This framework will enable modelers to set realistic reaction time values, relying on the simulator to handle implementation and optimization considerations. Following this, we report our findings demonstrating the impact of reaction time on traffic dynamics within several simulation scenarios. The findings indicate that in the incorporation of reaction time within microscopic simulations improves the traffic dynamics that produces more realistic traffic condition.Singapore-MIT Alliance for Research and Technolog