With the increasing of transit vehicles in urban networks, the traditional active transit signal priority control may fall short of efficiency due to the negative impact to non-priority approaches. In responds to the control need under such condition, this study presents an integrated signal progression model, named INTEBAND, to coordinate the platoon of passenger cars and bus vehicles along the arterial. The INTEBAND model integrates existing MULTIBAND and BUSBAND with the capability to balance the benefits of passenger car users and bus passengers. Taking Jinshi road in the city of Jinan, China as an example, VISSIM simulation modal is employed as an unbiased tool for model evaluations. Our further exploration with simulation experiments for sensitivity analysis has also found that INTEBAND will significantly reduce bus passenger delay and average person delay compared with conventional band controls if the ratio of bus passengers and passenger-car users exceeds the threshold of 1.5. Moreover, a multi-classifier model based on simulation results of 477 scenarios is to answer what conditions is suitable to apply INTEBAND control.

Transit signal priority (TSP) has proven to be a cost-effective solution for public transport (PT) vehicles at signalised intersections. Various studies have focused on the design and operation of TSP, while few have considered the optimum combination of TSP at an arterial and a network level. However, it is unclear whether the combination of TSP on an arterial or a network creates a multiplier effect on PT benefits, i.e. benefits from providing TSP at multiple intersections are higher than the sum of benefits from providing TSP at each of those individual intersections. If a multiplier effect exists, it suggests considerable impacts of TSP on a network-wide scale.

                This paper investigates effects of TSP combinations on arterials to establish if a multiplier effect exists. All possible spatial combinations of TSP at five intersections along an arterial are examined with various traffic demand and bus headway levels and offset settings, i.e. free-flow offsets and optimised offsets that minimise bus delay.

                Regression results show a non-linear relationship between the number of intersections with TSP and bus delay savings with optimised offsets and specific bus headways, indicating that providing TSP at multiple intersections can create a multiplier effect on bus delay savings. With free-flow offsets, the effect of TSP combinations on bus delay savings is linear. Results also suggest a linear effect of TSP combinations and a non-linear effect of traffic volume on the increase in side street traffic delay.

                Policy implications and areas for future research are suggested.

The paper evaluates the potential network-wide impacts of the Multi-Modal Intelligent Transportation Signal System (MMITSS) based on a field data analysis utilizing data collected from a MMITSS prototype and a simulation analysis. The Intelligent Traffic Signal System (I-SIG), Transit Signal Priority (TSP), Freight Signal Priority (FSP), and TSP/FSP bundle applications were evaluated. The field data analysis demonstrated that MMITSS applications effectively improved the travel time and the delay of the equipped vehicles. In particular, FSP reduced the delay of connected trucks by up to 20.9% and I-SIG reduced travel time variability by up to 56%, compared to the base case. The simulation study found that I-SIG achieved vehicle delay reductions of 20.6% and TSP effectively saved travel time for both transit and passenger vehicles on the corridor where TSP was operated; but occasionally increased the system-wide delay, due to reduced green times on the side streets. FSP simulation results indicated that FSP successfully reduced travel times by up to 20.5% for connected trucks. However, the FSP application also increased system-wide delay, due to increased delays on side streets. The simulation study found that the TSP/FSP bundle application was effective in assigning priority to trucks based on a pre-defined hierarchy of control. The study concludes that the MMITSS I-SIG, TSP, FSP, and TSP/FSP bundle applications improve vehicle travel time, delay, and travel time reliability for equipped passenger cars, trucks, and transit vehicles on the test facility, but may produce overall system-wide negative impacts.

Connected Vehicle (CV) technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) promise major benefits in both mobility and safety applications. One of the CV applications is connected traffic signal preemption for emergency vehicles enabling the rapid movement of emergency vehicles in urban arterials. This paper describes an innovative signal control strategy proposed to decrease Emergency Vehicle Response Time (EVRT). By employing V2I communication and IEEE 802.11p beaconing concept as well as the predicted queue length, traffic signals are adjusted adaptively to provide an early green at the right time so that the queue at the downstream intersections can be served just in time for the arrival of an emergency vehicle.  The strategy is implemented in the microscopic traffic simulator, SUMO and evaluated using the City of Toronto network. In addition, a Python-based program is developed to link the control strategy to SUMO for simulating the traffic with intelligent traffic signals. The simulation results show a significant reduction in EVRT using the proposed method.

The optimization of vehicle and pedestrian signals for an isolated intersection is a fundamental problem to improve transportation system efficiency. This paper presents a convex quadratic program model to optimize traffic signals for an isolated intersection with one- and two-stage crosswalks under undersaturated traffic. Both vehicle and pedestrian delays are considered in the optimization objective. The total delay is modeled by use of a convex quadratic formula. The number of waiting pedestrians on the refuge island at a two-stage crosswalk is modeled by use of a linear formula so that all constraints are in linear forms. The convex quadratic grogram model can be solved by many existing algorithms or softwares efficiently. The proposed model has three distinguishing features: ( a) the benefits of both vehicles and pedestrians are considered in a unified framework, ( b) the number of waiting pedestrians on the refuge island at a two-stage crosswalk is modeled in a linear form, and ( c) the proposed signal optimization model is a convex quadratic model that can be easily implemented in practice. The results of the case study showed that the proposed model would help traffic practitioners, researchers, and authorities optimize vehicle and pedestrian signals for an isolated intersection. The consideration of crossing pedestrians has an impact on the optimal signal cycle.

Many agencies around the U.S. are implementing transit preferential treatments, including transit signal priority (TSP), queue jumps, and queue bypass lanes, for transit vehicles operating in mixed traffic on arterials (e.g. bus or streetcar). However, the benefits and disadvantages of these treatments have not yet been quantified using a comprehensive corridor travel time analysis. This paper includes a VISSIM study of an existing transit corridor in Fort Lauderdale, Florida and generalizes the results for application to other sites. The assumptions of the study included average transit headways of five minutes, a 100-second signal cycle length, and that transit vehicles would always call for priority (either red truncation or green extension) at intersections with TSP. Each treatment was tested using volume-to-capacity ratios of 0.5, 0.8, and 1.0, and the performance measures included transit vehicle travel time, travel time for all approaching vehicles, and total intersection delay for all vehicles.

The results indicate that transit stop location, volume-to-capacity ratio, and type of treatment each have a significant effect on all three performance measures tested. Some of the principal findings are: a far side transit stop can reduce transit vehicle travel time by up to five percent over a near side stop, installing TSP in one direction tends to provide less negative effects to side street traffic than if TSP is installed in both directions, and TSP is most effective along a corridor if it is implemented only at moderately-congested intersections (i.e. those with a v/c ratio between 0.6 and 0.9).

Recent advancements in computer technology have provided more information to scientists than ever before. There is still a need to convert all this information into knowledge. Traffic signal controller systems have been impacted by this revolution as well. A graphical tool called the Purdue Coordination Diagram (PCD) leverages detailed event-based data to visualize signal controller operation, offering a new capability to develop insights about the performance. Analyzing alternative signal controller operations in the real world condition can be costly, especially for complicated features such as Transit Signal Priority (TSP) strategies that require substantial investments to deploy. Therefore, engineers often use microsimulation models to evaluate the effectiveness of complicated strategies prior to deployment. This study demonstrates a method to generate PCDs in the microsimulation level analysis. A computational engine is developed as part of this study that translates native microsimulation data into the equivalent event-based data needed to construct the PCDs. The proposed method is applied to a real world corridor in downtown San Antonio, Texas. The study site consists of 11 intersections, of which seven are signalized. TSP strategies are deployed in five intersections. Four operational scenarios are considered: No TSP; TSP with existing offsets; TSP with genetic algorithm optimized offsets; and TSP with hill climbing optimized offsets. The study demonstrates that the PCD can add valuable information to an evaluation of this sort. Specifically, the PCDs generated by the proposed methodology can help the user to evaluate/change TSP parameters or fine-tune the offset optimization solutions in the microscopic level.

Transit Signal Priority (TSP) control, a widely applied operation strategy that facilitate the movement of transit vehicles through traffic-signal controlled intersections, has been recognized as one of the most practical strategies to enhance efficiency and reliability of bus operations. With the increase on the road user’s value of time, an optimal TSP control is required not only to guarantee the operational efficiency of buses on priority approaches but also to reduce the total passengers delay over the entire intersection. To such need, the developed system shall integrate a function to wisely allocate the green time between priority phases and non-priority phases. In this paper, an optimization model is proposed to provide green time compensation to non-priority phases through a game theory approach. Also to demonstrate the effectiveness of the proposed model, this study conducts a case study on a field site in Beijing, China. The experimental results have proved the performance improvement of the target intersection with the proposed system.

The purpose of this paper is to define a visualization method to evaluate the performance of a multi-modal traffic signal system. Previous studies have concentrated on performance assessment for single modes, such as delay or travel time of passenger vehicles, or transit running times. The methodology presented in this paper considers an integrated approach to multi-modal performance assessment. A tool, called a Multi-Modal Performance Dashboard, is developed to visualize the relationship between various performance measures and multiple modes. Dashboards can be used to characterize the performance of an existing system and also to compare before and after studies when a new design is implemented. Radar diagrams are the basic element of the Multi-Modal Performance Dashboard tool and are constructed for performance measures, e.g. passenger vehicle travel time, transit delay, pedestrian volume, and truck stops, and for each movement at an intersection. An arterial corridor in the Maricopa County Department of Transportation's SMARTDrive test bed is analyzed using VISSIM micro-simulation model to study the effects of different designs and signal timing strategies on several performance measures for both vehicles and pedestrians. Based on the results of this study, choosing an appropriate control strategy can impact the different movements of different modes (including pedestrians) in a variety of ways. The more modes involved in the system, the more challenging it is to determine the proper control strategy. Using this comparative tool, alongside statistical models, makes it easier for decision makers to understand, visualize, and analyze data.

This paper proposes the use of a signal phase spectrum (SPS) plot to analyze high resolution traffic signal event data.  Specifically, the controller performance related to emergency vehicle preemption operation is characterized in an effort to identify performance measures that will allow a traffic engineer to better understand the impact that various configurations have on intersection operations.  Performance measures for individual intersections in coordinated systems including preemption duration, transition duration, and total interruption time.  Performance measures for networks are based on an emergency vehicle re-identification process for deriving an emergency vehicle’s trajectory through a network, and the results can further be used to estimate travel time, travel speed, and origin-destination.  These performance measures are illustrated using a simulated signal system in Morgantown, WV.  Transition modes are varied in the simulation network to determine the relative performance measures.  Case studies are presented for using high resolution data to troubleshoot field preemption operation using the Morgantown, WV and Huntington, WV signal systems.

The study develops the Multi-Modal Intelligent Transportation Signal System (MMITSS) simulation tool to assess the potential impacts of a broader MMITSS deployment, which will ultimately facilitate the site-independent analysis of MMITSS applications. The study also evaluates the effectiveness of the MMITSS application to identify the most beneficial operational conditions for each MMITSS operational scenario considering a combination of simulation variables and traffic demand levels. The MMITSS simulation platform consists of the VISSIM microscopic traffic simulation software, the Basic Safety Message (BSM) distributor program, the Econolite ASC/3 traffic controller emulator that runs on a Windows platform and a Road Side Equipment (RSE) Module that runs on a Linux platform. The tool is then used to conduct a simulation study of the Intelligent Traffic Signal System (I-SIG). The study demonstrates that I-SIG reduces vehicle delay by up to 35% and increases average traffic stream speed by up to 27%. In addition, Transit Signal Priority (TSP) is demonstrated to reduce travel time for both transit and passenger vehicles on the corridor by up to 29% and 28%, respectively. However, the TSP can increase system-wide delay because it reduces green times on the side streets. The study also demonstrates that Freight Signal Priority (FSP) can be effectively utilized along major freight routes. While the FSP significantly reduces truck delay, network-wide delay is increased substantially, especially in the high truck composition scenario. The TSP/FSP bundle simulation study demonstrates that the bundle operation successfully executed a hierarchical level of priority providing higher priority for trucks. The study concludes that overall MMITSS applications effectively improve vehicle travel time and delay for equipped and potentially non-equipped vehicles depending on the scenarios considered. Typically system-wide dis-benefits are observed when TSP, FSP, and TSP/FSP types of control are applied.