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This study develops an optimization methodology for signal timing at intersections to reduce emissions based on MOVES, the latest emission model released by U.S. Environmental Protection Agency (EPA). The primary objective of this study is to bridge the gap that the research on signal optimization at intersections lags behind the development of emissions models. The methodology development includes four levels: the vehicle level, the movement level, the intersection level, and the arterial level. At the vehicle level, the emission function with respect to delay is derived for a vehicle driving through an intersection. Multiple acceleration models are evaluated, and the best one is selected in terms of emission estimations at an intersection. Piecewise functions are used to describe the relationship between emissions and intersection delay. At the movement level, emissions are modeled if the green time and red time of a movement are given. To account for randomness, the number of vehicle arrivals during a cycle is assumed to follow Poisson distributions. According to the numerical results, the relative difference of emission estimations with and without considering randomness is usually smaller than 5.0% at a typical intersection of two urban arterials. At the intersection level, an optimization problem is formulated to consider emissions at an intersection. The objective function is a linear combination of delay and emissions at an intersection, so that the tradeoff between the two could be examined with the optimization problem. In addition, a convex approximation is proposed to approximate the emission calculation; accordingly, the optimization problem can be solved more efficiently using the interior point algorithm (IPA). The case study proves that the optimization problem with this convex approximation can still find appropriate optimal signal timing plans when considering traffic emissions. At the arterial level, emissions are minimized at multiple intersections along an arterial. First, discrete models are developed to describe the bandwidth, stops, delay, and emissions at a particular intersection. Second, based on these discrete models, an optimization problem is formulated with the intersection offsets as decision variables. The simulation results indicate that the benefit of emission reduction become more and more significant as the number of intersections along the arterial increases.

Driver violations at traffic signals are a major cause of intersection vehicle crashes. The yellow interval is used to inform approaching drivers of an upcoming change in the traffic signal indication from green to red. Current yellow-interval durations are currently calculated to accommodate for dilemma zone protection for passenger cars only. Buses with different vehicle, driver, and occupancy characteristics behave differently at the onset of a yellow indication. The research presented in this thesis characterizes the difference between bus and passenger car driver behavior at the onset of yellow-indication. A revised set of yellow timing procedures are presented to address the requirements for bus dilemma zone protection. A dataset of 864 stop-go records were collected as part of the research effort using a school bus approaching a traffic signal on the Virginia Smart Road facility. The experiment was conducted at an instructed speed limit of 57 km/h (35 mph) approach speed where participant drivers were presented with yellow indications. A total of 36 participating bus drivers were randomly selected from three age groups (under 40 years old, 40 to 64 years old and 65 and above) with equal number of male and female for each age group. Using the data collected as part of this research effort, statistical models were created to model bus driver perception-reaction times (PRTs) and deceleration levels considering driver attributes (age and gender), roadway grade, vehicle approach speed, and time to intersection (TTI) at the onset of the yellow indication. A Monte-Carlo simulation was conducted to develop appropriate yellow indication timings to provide adequate dilemma zone protection for buses. Lookup tables were then developed for different reliability levels to provide practical guidelines for the design of yellow signal timings to accommodate different bus percentages within the traffic stream. The recommended change durations can be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication. / Master of Science

Driver violations of traffic signals are a major cause of intersection vehicle crashes. The duration of yellow intervals is highly associated with driver yellow/red time stopping behavior. Rainy weather and wet pavement surface conditions may result in changes in both driver behavior and vehicle performance. The research presented in this thesis quantifies the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs) and deceleration levels, which are used in statistical models for the design of yellow intervals. A new dataset with a total of 648 stop-run records were collected as part of the research effort during rainy weather and wet pavement surface conditions at the Virginia Department of Transportation's Smart Road facility. This experiment was conducted at a 72.4 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Combined with an existing dataset that was collected by the same research group under clear weather conditions during the summer of 2008, statistical models for driver PRT and deceleration levels are developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, and time to the intersection at the onset of yellow. Using the state-of-the-practice procedures with the modeled PRT and deceleration levels, inclement weather yellow timings are then developed as a function of different factors (e.g., driver age/gender, roadway grade, speed limits, and precipitation levels). The results indicate that an increase in the duration of change interval is required for wet roadway surface and rainy weather conditions. Lookup tables are developed with different reliability levels to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. These recommended change durations can be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication. / Master of Science

Traffic simulation and signal timing optimization are classified in structure into two main categories: (i) Macroscopic or Microscopic; (ii) Deterministic or Stochastic. Performance of the optimized signal timing derived by any tool is influenced by the methodology used in how calculations are executed in a particular tool. In this study, the performance of the optimal signal timing plans developed by two of the most popular traffic analysis tools, HCS and Tru-Traffic, each of them has its inbuilt objective function(s) to optimize signal timing for intersection, is compared with an ideal and an existing timing plans (base case) for the area of study using the microsimulation software VISSIM. An urban arterial with 29 intersections and high traffic in Fort Lauderdale, Florida serves as the test bed. To eliminate unfair superiority in the results, all experiments were performed under identical geometry and traffic conditions in each tool. Comparison of the optimized plans is conducted on the basis of average delay, average stopped delay, average number of stops, number of vehicles completed trips, latent delay, and latent demand from the simulated vehicle network performance evaluation results in VISSIM. The results indicate that, overall, HCS with its overall delay objective and the Tru-Traffic programs produce signal timing with comparable quality that performed similar to the un-optimized base case for most of the performance measures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Signal retiming, or signal optimization process, has not changed much over the last few decades. Traditional procedures rely on low-resolution data and a low-fidelity modeling approach. Such developed signal timing plans always require a fine-tuning process for deployed signal plans in field, thus questioning the very benefits of signal optimization. New trends suggest the use of high-resolution data, which are not easily available. At the same time, many improvements could be made if the traditional signal retiming process was modified to include the use of medium-resolution data and high-fidelity modeling. This study covers such an approach, where a traditional retiming procedure is modified to utilize large medium-resolution data sets, high-fidelity simulation models, and powerful stochastic optimization to develop robust signal timing plans. The study covers a 28-intersection urban corridor in Southeastern Florida. Medium-resolution data are used to identify peak-hour, Day-Of-Year (DOY) representative volumes for major seasons. Both low-fidelity and high-fidelity models are developed and calibrated with high precision to match the field signal operations. Then, by using traditional and stochastic optimization tools, signal timing plans are developed and tested in microsimulation. The findings reveal shortcomings of the traditional approach. Signal timing plans developed from medium-resolution data and high-fidelity modeling approach reduce average delay by 5%-26%. Travel times on the corridor are usually reduced by up to 10.5%, and the final solution does not transfer delay on the other neighboring streets (illustrated through latent delay), which is also decreased by 10%-49% when compared with the traditional results. In general, the novel approach has shown a great potential. The next step should be field testing and validation. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Less than 1% of work and school trips are completed by bicycle in the United States. Comprehensive bicycle facilities improve bicycle ridership by including a diverse set of strategies that accommodate the bicycle mode and seek to minimize the Level of Traffic Stress experienced by riders. Traffic Signal Operational Strategies for Bicycles (TSOSB) are an integral component of comprehensive bicycle facilities. This research presents a methodology to identify critical zones for implementation of TSOSB. After identifying critical zones a process for assessment of gaps in bicycle safety and comfort and convenience for signalized intersections within the critical zones is conducted. The outcome of the methodology is a prioritized list of signalized intersection that could benefit from the application of Traffic Signal Operational Strategies for Bicycles

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1996. / Vita. Abstract. Includes bibliographical references (leaves 161-166). Also available via the Internet.

<p>   </p> <p>Historically, roadway performance measures have focused almost exclusively on vehicular movement. In most urban settings, pedestrian movements typically outnumber vehicular movements significantly. However,  historically there has been no way to collect such data at scale in a systematic manner. With the widespread introduction of cameras for monitoring vehicular flow, there is an opportunity to leverage this infrastructure to acquire insights into the patterns and trends of pedestrian activities at signalized intersections in an automated and systematic manner. Such data and performance measures are critical inputs for detailed analysis of pedestrian movements. Overall, addressing this issue is a vital component of transportation agencies that seek to develop equitable treatment of all transportation system users including vulnerable road users. This dissertation addresses the gap in the literature regarding detailed characterization of pedestrian movement patterns and trends. The dissertation leverages data from signalized intersection cameras to (1) quantify the required duration for the pedestrian walk-interval based on pedestrian volume and geometric features of the intersection, (2) carry out time series analysis to acquire insights on pedestrian demand patterns and the influential variables, and (3) build machine learning algorithms to accurately predict pedestrian volumes and tie it to signal timing, to enhance service for all roadway users.</p> <p>The first study provides quantitative guidance for walk time interval selection. This part reports on 1,500 pedestrian movement observations from 12 signalized intersections with varying pedestrian demand, pedestrian storage areas, and pedestrian push-button locations. That data were used to develop a model predicting start-up time with an R2 of 0.89. The study concludes by presenting a quantitative table with four timing categories ranging from negligible volume to high volume and corresponding appropriate durations for the pedestrian walk interval time, based on the demand per cycle, storage area for pedestrians, and offset of the pedestrian push-button from the crosswalk.</p> <p>The second study describes several scalable techniques for measuring and analyzing the movement of pedestrians on a typical university campus. Approximately 35.6 million pedestrian movements over 19 months were tabulated in 15-minute counts of pedestrian volumes by intersection. Counts are used in evaluating pedestrian activity dependency on select explanatory variables at both the network and intersection levels at each time step for the entire analysis period. The study reports on time series correlation and cross-correlation and measures the time-dependency between pedestrian activities and influential factors such as the academic calendar, football games, basketball games, and graduation ceremonies. It provides a comprehensive understanding of the factors that are most influential of  pedestrian volumes at intersections.</p> <p>The third study presents a data-driven approach to predict pedestrian volume per intersection quadrant at 15-minute intervals, and to connect this information to signal timing. Machine learning random forest and XGBoost classification models were trained on a large dataset of pedestrian counts consisting of approximately 2.6 million observations collected through 19 months at 13 exclusive pedestrian service intersections. The predicted pedestrian volumes were then categorized per the pedestrian walk-interval categories to provide optimal signal timing for each intersection quadrant, thus enabling potential dynamic pedestrian signal timing at exclusive service intersections. The results of this study showed that the developed models accurately predict pedestrian volumes per 15-minute intervals for each quadrant of an intersection, with a high degree of precision and a prediction accuracy of 82.3%. Signal timing optimization based on predicted pedestrian volume can significantly improve pedestrian mobility and maximize traffic flow. </p> <p>The findings of this study provide valuable insights for traffic engineers and planners interested in developing and deploying dynamic pedestrian signal timing systems. It is a practical and effective solution for improving mobility for all roadway users at intersections with exclusive pedestrian service.</p> <p>  </p>

Continuous transportation demand growth in recent years has led to many traffic issues in urban areas. Among the most challenging ones are traffic congestion and the associated vehicular emissions. Efficient design of traffic signal control systems can be a promising approach to address these problems. This research develops a real-time signal control system, which optimizes signal timings at an under-saturated isolated intersection by minimizing total vehicular emissions. A combination of previously introduced analytical models based on traffic flow theory has been used. These models are able to estimate time spent per driving mode (i.e., time spent accelerating, decelerating, cruising, and idling) as a function of demand, vehicle arrival times, saturation flow, and signal control parameters. Information on vehicle activity is used along with the Vehicle Specific Power (VSP) model, which estimates emission rates per time spent in each operating mode to obtain total emissions per cycle. For the evaluation of the proposed method, data from two real-world intersections of Mesogion and Katechaki Avenues located in Athens, Greece and University and San Pablo Avenues, in Berkeley, CA has been used. The evaluation has been performed through both deterministic (i.e. under the assumption of perfect information for all inputs) and stochastic (i.e. without having perfect information for some inputs) arrival tests. The results of evaluation tests have shown that the proposed emission-based signal control system reduces emissions compared to traditional vehicle-based signal control system in most cases.