16-06 Vehicle-to-Device (V2D) Communications: Readiness of the Technology and Potential Applications for People with Disability

IEEE 802.11p was developed as an amendment to IEEE 802.11 for wireless access in vehicular environments (WAVE). While WAVE is considered the de facto standard for V2V communications, in the past few years a number of communications technologies have emerged that enable direct device-to-device (D2D) communications. Technologies like Bluetooth Smart, WiFi-Direct and LTE-Direct allow devices to communicate directly without having to rely on existing communications infrastructure (e.g., base stations). More importantly, these technologies are quickly penetrating the smartphones market. The goal of this research is to conduct extensive simulation and experimental studies to assess the efficacies of utilizing D2D communications technologies in transportation scenarios focused around pedestrians and bicyclists. Specifically, we design, develop, and experiment with Smart Cone and Smart Cane systems to evaluate the readiness of D2D technologies to support transportation applications

Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

Safety assessment of pedestrian-vehicle interaction at signalized intersections: An observational study

Road safety is a crucial aspect of global policies and management. Surrogate Safety Measures (SSMs) have gained attention in the study of pedestrian safety. This study aims to establish an effective SSM methodology to analyze driver-pedestrian interactions. The analysis relies on SSM indicators, without the need for an initial classification of driver-pedestrian interactions into specific interaction patterns. The proposed methodology offers several advantages, including the accurate identification of conflicts through an affordable approach making it easily accessible for public administrations and authorities to assess pedestrian safety at road intersections. A dataset comprising 270 driver-pedestrian interactions, observed at three road intersections in Rome, Italy, was examined. The severity level of each event was assessed through a preliminary classification of each interaction into three patterns: high, low, and none. Subsequently, the severity levels were evaluated using three methods, employing Time-to-Collision (TTC), Post-Encroachment Time (PET), and a combination of TTC and PET. A comparison between the severity levels identified by the two approaches was conducted. The findings reveal that Method 2, utilizing PET, consistently identifies conflicts. Additionally, a binomial logistic regression analysis was performed to identify the variables that influence the likelihood of an interaction escalating into a conflict. The results demonstrate that the probability of conflict increases with the duration of a red signal, particularly for younger pedestrians

Studies of Driver Behaviors and Traffic Flow Characteristics at Roadway Intersections

The performances of intersections and driveway access points are crucial to a road network in terms of efficiency and safety. Driver behavior and traffic flow characteristics at these locations are relatively complex. To better understand these issues and potentially provide guidance to engineers in their designs, a series of studies were performed on the driver behavior and traffic characteristics at intersections and driveway access points based on field experiments or observations. First, a countdown timers study was performed in China about their influences on driver behavior. It was found that the presence of countdown timers may encourage yellow running behavior and late entry into intersections in China. Second, a phase gradient method was proposed for the general application purpose to the studies of driver behavior and traffic characteristics at signalized intersections. A case study on red-light cameras was performed at Knoxville, TN. Third, a study was performed to learn the legal issues and arguments about the usage of red-light cameras for the purpose of generating profits. A variety of engineering measures, mainly dealing with the setting of the traffic signal, which could be potentially used by municipalities or camera vendors to trap red-light runners and thus generating more revenues from the camera system are discussed. Finally, an experiment was conducted to simulate the right-turn issues, which impact the safety and operation efficiency at intersections or driveway access points. Two turn lane geometric parameters, angle-of-turn and tangent, and their influences on driver behavior and traffic flow characteristics were studied

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

An Overview of Vehicle-to-Infrastructure Communication Technology

As a part of solutions to reduce problems associated with transportation in cities, technologies can have noticeable impacts. Due to efficiency and low costs, innovative transportation technologies can reshape and improve human’s transportation. This research aims to explore Vehicle-to-Infrastructure communication technology (V2I) and its benefits to safety, mobility, and environment. In addition, it explores the planning aspect of deploying V2I technology and its opportunities, challenges and concerns, and implication to communities. The research will also look at several case studies including pilot projects that have been taking place in the United States and studies that have been done to have a better understanding of the current situation of V2I technology and its future needs. Advisor: Rodrigo Cantarer

Waiting for signalized crossing or walking to footbridge/underpass? Examining the effect of weather using stated choice experiment with panel mixed random regret minimization approach

It is a challenging task for pedestrians to cross a road with multiple traffic lanes and busy traffic. Many footbridges and underpasses have been built in the urban area of metropolitan cities such as Hong Kong to resolve the problem of vehicle-pedestrian conflict. To maximize the utilization and benefit of the installation of such crossing facilities, it is crucial to understand the choice behaviour of pedestrians. Although many studies have examined pedestrian walking behaviour and preference towards crossing facilities, the influence of ratio of perceived values between waiting and walking time on the choice of crossing is not explored. In addition, individual perception and choice may vary with the environmental conditions, which has not been fully accounted for in existing studies. Exposure to extremely hot weather, crowded walkways, and roadside traffic emissions are not favoured. In this study, a stated choice experiment is developed to examine the relationship between possible influencing factors and the crossing choices of pedestrians in Hong Kong. In addition, a regret-based panel mixed multinomial logit approach is adopted to model the choice, accounting for the effects of unobserved heterogeneity and panel data. The results indicate that the choice decision of pedestrians is more sensitive to an increase in waiting time at signalized crossings than to an increase in walking time to access footbridges and underpasses. These findings shed light on future urban and transport planning strategies to improve the walking environment and promote walkability