Optimization Model of Transit Signal Priority Control for Intersection and Downstream Bus Stop

Arterial signal coordinated control system and transit signal priority strategy are widely used for decreasing delay and improving the level of service in the urban areas, therefore, transit signal priority (TSP) optimization under arterial coordinated control condition needs to be focused on for improving urban traffic efficiency. This paper develops an arterial signal coordinated control method regarding TSP scenarios considering passenger delay and transit schedule adherence simultaneously, and a passenger-based MAXBAND graphic method is proposed to a generate coordinated control plan. Consequently, a test arterial segment of Nanjing (in China) is simulated by the VISSIM software platform to evaluate and analyze the performance of the proposed optimization method in decreasing delay and improving system reliability. The results illustrate that the proposed optimization model presents promising outcomes in transit signal priority under signal coordinated control conditions compared with traditional coordinated optimization methods.

Some urban road tunnels serve as the main traffic access routes for residents in the surrounding large communities. The traffic congestion is a common phenomenon during rush hours inside this kind of tunnel. In order to learn about the environmental parameters inside tunnel during the evening rush hour, a comprehensive measurement in a typical commuting tunnel is conducted. The thermal environment parameters and air pollutant distribution along the tunnel are measured simultaneously. The results show that the traffic volume, vehicle speed and air velocity inside tunnel take on the obvious temporal and spatial discrepancy. There are the average 4.58°C air temperature rise and 96μg/m3 PM2.5 concentration rise from the tunnel entrance to the tunnel exit. The PM2.5 concentration at the tunnel exit is about 2.67 times of the 24-hour average limit of Chinese standard. The rise gradients of the air temperature and PM 2.5 concentration during the evening rush hour is given out.

Bus rapid transit (BRT) as a valid means of public transportation for overpopulated country of China is an especial mode of travel to relieve traffic congestion. Despite utilizing exclusive bus line, BRT constantly experiences delay at intersections when encountering a red signal. Transit signal priority (TSP) considered as a promising control strategy to improve the operation efficiency of BRT has been widely used at isolated intersections or arterials to reduce travel delay. However, as a transport with schedule, the unexpected arrival time of transit vehicle at stop lines of consecutive intersections inevitably affects the operation reliability of the BRT system. To solve the problem, this study develops an optimization model of conditional TSP for bus rapid transit with the objective of improving the on-time performance of the BRT system while mitigating adverse impacts on private vehicles. A stop-to-stop segment is established with both road sections and intersections for the consideration of the transit operation reliability. The constraints on the degree of saturation and queue overflow were considered for mitigating adverse impacts of TSP. A mixed-integer non-linear programming procedure is adopted to formulate the optimization model. The mathematical model is linearized and solved by the branch-and-bound method. Extensive numerical analyses are conducted, and the proposed conditional TSP strategy is compared with unconditional TSP and no-TSP control to evaluate its performance under various traffic and signal conditions. A case study of a BRT line in Shanghai, China, was selected to illustrate the effectiveness of the proposed model. For the tested scenarios, the transit on-time performance was improved by 21%, with an incurred cost of 3.4% increase in the delay of private vehicles. This indicates that the proposed model performs well in maintaining the reliability of the transit system with the least impact on general traffic.

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.

Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings

This study addresses the traffic signal optimization problem at a median-west median intersection. Based on 36 days of time-segmented traffic volume data, it combines the NS traffic flow model, Webster signal timing model, and genetic algorithm to propose a dynamic phase control scheme. By analyzing the traffic characteristics during morning rush hour, evening rush hour, off-peak hours, and nighttime, differentiated signal phases are designed: high-flow turning maneuvers (such as U-turns and left turns) are prioritized during peak hours, combined phases are added during off-peak hours, and induction control is used at night. After optimization, the average speed on the main road during morning rush hour increases from 28 km/h to 42 km/h, with delays reduced by 35%, and off-peak traffic capacity improves by 25%. The scheme achieves maximum traffic flow speed on the main road through phase conflict avoidance, green wave coordination, and dynamic parameter adjustments, providing a data-driven and model-coordinated solution for optimizing complex urban intersections.

Ambient concentrations of C2-C12 volatile organic compounds (VOCs) were measured at Bursa, which is the fourth largest city of Turkey. Two measurement campaigns were carried out between September 14 to November 6, 2005 and March 17 to May 10, 2006. Fifty one VOCs were regularly determined in each chromatogram. However, only BTX compounds are discussed in this study. Concentrations of BTX compounds showed well-defined diurnal cycles. Concentrations increased starting at 06: 00 am in the morning, reaching to a maximum somewhere between 9: 00 to 11: 00 am. Then concentration decreased, until the evening rush hour, owing to increasing mixing height during day time. Then concentrations of BTX increased in the afternoon rush hour which extended to 08: 00 pm. BTX concentrations remained low throughout the night. High binary correlations between BTX compounds suggest a common source, which are traffic emissions. However, other non-traffic sources were also found to be effective, for benzene in the November to March campaign.

Network Congestion, Braess Paradox and Urban Expressway System

Kathmandu Valley, Nepal, has severe air pollution, although few studies examine air pollution and health in this region. To the best of our knowledge, no previous studies in Nepal used time-activity diaries or conducted personal monitoring of individuals' exposures. We investigated personal exposure of particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM(2.5)) by location, occupation, and proximity to roadways. PM(2.5) monitoring, time-activity diary, respiratory health questionnaire, and spirometer testing were performed from 28 June 2009 to 7 August 2009 for 36 subjects, including traffic police (TP), indoor officer workers next to main road (IOWs_NMR) and away from main road (IOWs_AMR), in urban area (UA), urban residential area, and semi-UA (SUA). TP had the highest exposure of all the occupations (average 51.2 μg/m(3), hourly maximum >500 μg/m(3)). TP levels were higher at the UA than other locations. IOW_NMR levels (averaged 46.9 μg/m(3)) were higher than those of IOW_AMR (26.2 μg/m(3)). Exposure was generally higher during morning rush hours (0800-1100 hours) than evening rush hours (1500-1800 hours) for all occupations and areas (78% of days for TP and 84% for urban IOW). PM(2.5) personal exposures for each occupation at each location exceeded the World Health Organization ambient PM(2.5) guideline (25 μg/m(3)). Findings suggest potential substantial health impacts of air pollution on this region, especially for TP.

The study analyzed aspects of reducing the environmental impact of transport based on reducing the time to find parking for a car. The time spent by drivers searching for parking in the central part of the metropolis was analyzed. Data on the amount of time spent was obtained by interviewing drivers. The survey was conducted after completing a task to find parking in the central part of the metropolis at a certain time of the day (morning rush hour, daytime, evening rush hour). The data obtained reflect the time spent by drivers searching for parking in the central part of megacities; the data allows us to estimate the amount of environmental load arising from the “useless” movement of vehicles in the center of the metropolis, arising from the lack of free parking spaces. These studies can be used in the development of parking space planning programs and environmental programs aimed at improving the environmental situation in cities.

Abstract. Measurements of atmospheric boundary layer nitrous acid (HONO) and nitrogen oxides (NOx) were performed in summer 2016 inside a city centre road tunnel in Birmingham, United Kingdom. HONO and NOx mixing ratios were strongly correlated with traffic density, with peak levels observed during the early evening rush hour as a result of traffic congestion in the tunnel. A day-time ΔHONO∕ΔNOx ratio of 0.85 % (0.72 % to 1.01 %, 95 % confidence interval) was calculated using reduced major axis regression for the overall fleet average (comprising 59 % diesel-fuelled vehicles). A comparison with previous tunnel studies and analysis on the composition of the fleet suggest that goods vehicles have a large impact on the overall HONO vehicle emissions; however, new technologies aimed at reducing exhaust emissions, particularly for diesel vehicles, may have reduced the overall direct HONO emission in the UK. This result suggests that in order to accurately represent urban atmospheric emissions and the OH radical budget, fleet-weighted HONO∕NOx ratios may better quantify HONO vehicle emissions in models, compared with the use of a single emissions ratio for all vehicles. The contribution of the direct vehicular source of HONO to total ambient HONO concentrations is also investigated and results show that, in areas with high traffic density, vehicle exhaust emissions are likely to be the dominant HONO source to the boundary layer.

The bridge spanning the Ohio River between Point Pleasant, W. Va., and Gallipolis, Ohio, known as the Silver Bridge, was designed and built during 1927–28. It was the first eyebar suspension bridge in the United States, and it received much attention for making engineering history. After some 40 years of service, the bridge collapsed without warning on December 15, 1967 during the evening rush hour, when the bridge was crowded with heavy traffic. The collapse resulted in the loss of 46 lives and nine injuries. A thorough investigation revealed that the collapse of the bridge was caused by the failure of the north eyebar of the north chain at the first panel point west of the Ohio tower. The eyebar had developed a cleavage failure at the lower position of its head. The tragedy of this bridge failure led to the approval of the 1968 National Bridge Inspection Standards by the U.S. Congress.

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

Numerical investigation of collapse of the Minnesota I-35W bridge