Intersection Signal Control and Design for Improved Person Mobility and Air Quality in Urban Multimodal Transportation Systems

Alternative geometric designs (e.g. roundabouts) and multi-objective signal control strategies are promising measures to improve sustainability of traffic networks. However, roundabouts are mostly used because of their safety and operational advantages. There has been less attention to the environmental performance of roundabouts. Also, the existing studies have been mostly used through field measurements and current simulation models, which need high calibration efforts and they are not inclusive in terms of considering all influencing factors on vehicular emissions at roundabouts. Furthermore, the existing real-time signal control strategies do not account for the emission rates of different vehicle types (e.g. cars and buses). In addition, the real-time multi-objective signal control systems does not consider environmental objectives. This dissertation develops a real-time bi-objective signal control system for isolated intersections, which operate at undersaturated traffic conditions that minimizes a weighted combination of vehicle delay (or person delay) and emissions of auto and transit vehicles. Pareto Frontiers of the optimal solutions are presented to help decision makers select the most appropriate combinations of objectives to achieve desirable levels of delay and emissions. Additionally, a simple simulation tool based on Cellular Automata (CA) model of traffic simulation is developed to estimate delay and reproduce vehicle trajectories for emission estimation. The models are used to compare the operational and environmental performance of roundabouts and signalized intersections and perform sensitivity analysis with respect to total traffic demand, left turn ratio, and pedestrian volume. Evaluation tests show that replacing a signalized intersection with a roundabout results in improved delay and emissions at undersaturated traffic conditions and any pedestrian volume. It also shows that roundabouts’ performance is less affected by high left turning demand compared to signalized intersections. On the contrary, roundabouts’ performance is sensitive to frequent pedestrian crossings while the performance of signalized intersections is not affected by pedestrian crossings

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

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Estimation of delay and vehicle stops at signalized intersections using artificial neural network

Delay and number of vehicle stops are important indicators that define the level of service of a signalized intersection. Therefore, they are usually considered for optimizing the traffic signal timing. In this study, ANNs are employed to model delay and the number of stops estimation at signalized intersections. Intersection approach volumes, cycle length and left turn lane existence were utilized as input variables since they could easily be obtained from field surveys. On the other hand, the average delay and the number of stops per vehicle were used as the output variables for the ANNs models. Four-leg intersections were examined in this study.  Approach volumes including turning volumes are randomly generated for each lane of these intersections, then the traffic simulation program was run 196 times with each generated data. Finally, average delay and the number of stops per vehicle were obtained from the simulations as outputs. In this study, various network architectures were analyzed to get the best architecture that provides the best performance. The results show that the ANNs model has potential to estimate delays and number of vehicle stops