A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

Transit Preferential Treatments at Signalized Intersections: Person-based Evaluation and Real-Time Signal Control

Efficient public transportation has the potential to relieve traffic congestion and improve overall transportation system performance. In order to improve transit services, Transit Preferential Treatments (TPT) are often deployed to give transit vehicles priority over other vehicles at an intersection or along a corridor. Examples of such treatments are exclusive bus lanes, queue jumper lanes, and signal priority strategies. The objective of this study is threefold: 1) perform a person-based evaluation of alternative TPTs when considered individually and in combination, 2) develop a bus travel time prediction model along a signalized arterial, and 3) develop a real-time signal control system, which minimizes total person delay at an isolated intersection accounting for stochasticity in transit vehicle arrivals. This study first develops analytical models to estimate person delay and person discharge flow when various spatial and time TPTs are present at signalized intersections with and without near-side bus stops. This part of the research has contributed to the modeling of traffic along signalized arterials by improving the previous models to evaluate various TPT strategies with and without nearside bus stops. Next, a robust method to predict bus travel time along a signalized arterial is developed. This part of the research contributes to the bus travel time prediction models by estimating the status of traffic signals using automated vehicle location (AVL) data. The model decomposes bus travel time along signalized arterials and infers trajectories of the transit vehicles. Finally, the real-time signal control system is developed to provide priority to transit vehicles by assigning weights to transit vehicle delays based on their passenger occupancies as part of the optimization objective function. The system optimizes the movements by minimizing total person delay at the intersection. The system estimates bus arrival time at the intersection stopline and uses the developed analyitical models in the first part of the research to evaluate the person delay measure. This part of the research contributes to the real-time signal control systems by providing a priority window to account for the stochasticity in bus arrival times

Dynamic Vehicular Trajectory Optimization for Bottleneck Mitigation and Safety Improvement

Traffic bottleneck is defined as a disruption of traffic flow through a freeway or an arterial, which can be divided as two categories: stationary bottleneck and moving bottleneck. The stationary bottleneck is mainly formed by the lane drops in the multi-lane roadways, while the moving bottleneck are due to the very slowing moving vehicles which disrupt the traffic flow. Traffic bottlenecks not only impact the mobility, but also potentially cause safety issues. Traditional strategies for eliminating bottlenecks mainly focus on expanding supply including road widening, green interval lengthening and optimization of intersection channelization. In addition, a few macroscopic methods are also made to optimize the traffic demand such as routing optimization, but these studies have some drawbacks due to the limitations of times and methodologies. Therefore, this research utilizes the Connected and Autonomous Vehicles (CAV) technology to develop several cooperative trajectory optimization models for mitigating mobility and safety impact caused by the urban bottlenecks. The multi-phases algorithms is developed to help solve the model, where a multi-stage-based nonlinear programming procedure is developed in the first phase to search trajectories that eliminate the conflicts in the bottleneck and minimize the travel time and the remaining ones refine the trajectories with a mixed integer linear programming to minimize idling time of vehicles, so that fuel consumption and emissions can be lowered down. Sensitivity analyses are also conducted towards those models and they imply that several indices may significantly impact the effectiveness and even cause the models lose efficacy under extreme values. Various illustrative examples and sensitivity analyses are provided to validate the proposed models. Results indicate that (a) the model is effective to mitigate the mobility and safety impact of bottleneck under the appropriate environment; (b) the model could simultaneously optimize the trajectories of vehicles to lower down fuel consumption and emissions; (c) Some environment indices may significantly impact the models, and even cause the model to lose efficacy under extreme values. Application of the developed models under a real-world case illustrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating the intelligent transportation management

Evaluation of the Performance of the Sydney Coordinated Adaptive Traffic System (SCATS) on Powell Boulevard in Portland, OR

The Sydney Coordinated Adaptive Traffic System (SCATS) is used to mitigate traffic congestion along urban arterial corridors. Although there has been research on SCATS\u27 performance, this report combines three different areas of research about SCATS that are not known to be represented in any research literature. These include: (a) the relationship between SCATS, traffic volumes, and Transit Signal Priority (TSP); (b) between TSP and traffic conditions; and (c) the correlation between signal timing and air quality; in particular, human exposure to the air pollutant PM2.5 at intersections. In addition, this research looked at the key factors affecting transit user exposure to traffic-related pollutants at bus shelters. All areas of study present the results of statistical tests and regressions to determine SCATS or traffic variables impacts. SCATS did show statistically significant improvements regarding traffic speeds at one minor intersection, even when traffic volumes showed a statistically significant improvement. At a major intersection, results were mixed and not conclusive. Overall, it was determined that the improvements available through SCATS vary depending on the time of day and the direction of travel. TSP was not negatively affected by SCATS. In controlling for both priority and traffic conditions, each were shown to have a distinguished and significant impact on bus travel time. Non-priority signals had a much greater impact on travel time than priority signals (11.0 and 0.6 seconds for the corridor model, respectively). In controlling for both priority and traffic conditions, each were shown to have a distinguished and significant impact on travel time. Utilizing a regression model, results in an intuitive ranking of the intersections’ delay was produced; major intersections with high traffic volumes on crossing streets are likely to not experience TSP benefits. To a high degree, this research has shown that pedestrian exposure can be considered as an outcome of traffic-signal timing decisions made by cities and counties. The statistical results have shown the high impact that signal timing and queuing have on pedestrian level exposure. Heavy vehicle volume was a significant variable as well as the presence of buses. The reduction of bus idling time through more efficient operations and transit-signal priority is likely to reduce pedestrian and transit users\u27 pollution exposure levels. Longer green times along the main corridor are able to significantly reduce particulate matter for transit users and pedestrians waiting at the sidewalk of the intersection, whereas time allocated to cross the street increases queuing and exposure along the main corridor. The impact of heavy-duty diesel engines is also clear. The reduction of bus idling time through more efficient operations and transit-signal priority is likely to reduce pedestrian and transit users\u27 pollution exposure levels. Transit agencies can also reduce pollution significantly by improving the efficiency and cleanliness of their engines. TriMet (the local transit agency) initiatives to improve fuel efficiency by installing EMP engine-cooling devices not only improve fuel efficiency, but also air quality. Finally, significant reductions in transit users’ exposure to traffic-related pollution can be made at bus stops by properly orienting the shelter and by reducing bus idling

Alternative Intersection Design Strategies: How Georgia and the U.S. are Changing Outdated Transportation Design Techniques

The most deadly locations on our roads are the intersections. A 2008 study found that stop‐controlled intersections were responsible for 70% of the deaths on United States roadways that year. The alarming significance of one particular aspect of the transportation system having such a negative effect on human safety year after year has propelled reconsideration into the design strategies of our roadway intersections and have fueled the need for options in design as opposed to one scripted method. Local and national examples of alternative design strategies are occurring at a faster rate, further demonstrating that the strengths and weaknesses associated with each strategy are largely dependent on sitespecific circumstances. This paper presents a myriad of case studies that outline the successful implementation of alternative design strategies in addition to the local circumstances that made them successful. It is the purpose of this study to demonstrate the new standard of alternative design considerations along with developed examples of those still less‐common intersection types. These deliberations are conducted in an effort to combat investment fears and promote a more successful and appropriate design of our transportation system.Dobbins, Michae

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Increasing Capacity of Intersections with Transit Priority

Dedicated bus lane (DBL) and transit signal priority (TSP) are two effective and low cost ways in improving the reliability of transits. On the contrary, these strategies reduce the capacity of general traffic. This paper presents an integrated optimization (IO) model to improve the performance of intersections with dedicated bus lanes. The IO model integrated geometry layout, main-signal timing, pre-signal timing and transit priority. The optimization problem is formulated as a Mix-Integer-Non-Linear-Program (MINLP) that can be transformed into a Mix-Integer-Linear-Program (MILP) and then solved by the standard branch-and-bound technique. The applicability of the IO model is tested through numerical experiment under different intersection layouts and traffic demands. A VISSIM microsimulation model was developed and used to evaluate the performance of the proposed IO model. The test results indicate that the proposed model can increase capacity and reduce delay of general traffic when providing priority to buses

Experimental analysis of eGLOSA and eGLODTA transit control strategies

Battery powered electric buses have higher energy efficiency, lower emissions and noise when compared to buses with internal combustion engines. However, due to battery charging requirements, their large-scale integration into public transport operations is more complex. This study proposes a novel concept supporting said integration via new control strategies, dubbed e-GLOSA and e-GLODTA. These strategies extend the existing Green Light Optimal Speed and Dwell Time Systems (GLOSA/GLODTA) to account for the specific needs of electric buses. That is, they include the goals of minimizing the energy consumption between charging stations, and maximizing available charging time. At the same time, interference with schedule requirements is minimized. The formulated heuristics are tested on a Bus Rapid Transit (BRT) corridor case study, where different scenarios—such as placement of charging stations and bus regularity—are studied to assess under which conditions each action (maintain speed, accelerate or dwell for a longer time at a stop) is beneficial. Results show that eGLOSA contributes to schedule adherence while eGLODTA allows satisfying charging time constraints

Safety in Numbers: Models of Pedestrian and Bicycle Crash Frequency and Severity at Signalized Intersections in Utah Using Innovative Measure of Exposure

Recent trends indicate a dramatic increase in both the number and share of pedestrian and bicyclist injuries and fatalities nationally and in many states. This study aimed at understanding (geometric, traffic, operational, and other) factors associated with pedestrian and bicycle safety and also to assist in the prioritization and selection of counter measures to improve pedestrian and bicycle safety at signalized intersections. Several negative binomial models were estimated to investigate factors affecting pedestrian and bicycle crash frequency. The models suggested several characteristics of the road network, land use, built environment, and neighborhood sociodemographics were significantly associated with more (or fewer) pedestrian and bicycle crashes. Ordered logit models were fitted to investigate factors affecting injury severity in pedestrian and bicycle crashes. The model results indicated that vehicle size, vehicle maneuvering direction, and involvement of teenage/older drivers and DUI/drowsy/distracted driving in crashes had significant effects on injury severity in pedestrian and bicycle crashes. The study also found strong support for the “safety in numbers” effect, in which pedestrian/bicycle crash rates decrease with an increase in pedestrian/bicycle volumes