Simulating the Impact of Traffic Calming Strategies

This study assessed the impact of traffic calming measures to the speed, travel times and capacity of residential roadways. The study focused on two types of speed tables, speed humps and a raised crosswalk. A moving test vehicle equipped with GPS receivers that allowed calculation of speeds and determination of speed profiles at 1s intervals were used. Multi-regime model was used to provide the best fit using steady state equations; hence the corresponding speed-flow relationships were established for different calming scenarios. It was found that capacities of residential roadway segments due to presence of calming features ranged from 640 to 730 vph. However, the capacity varied with the spacing of the calming features in which spacing speed tables at 1050 ft apart caused a 23% reduction in capacity while 350-ft spacing reduced capacity by 32%. Analysis showed a linear decrease of capacity of approximately 20 vphpl, 37 vphpl and 34 vphpl when 17 ft wide speed tables were spaced at 350 ft, 700 ft, and 1050 ft apart respectively. For speed hump calming features, spacing humps at 350 ft reduced capacity by about 33% while a 700 ft spacing reduced capacity by 30%. The study concludes that speed tables are slightly better than speed humps in terms of preserving the roadway capacity. Also, traffic calming measures significantly reduce the speeds of vehicles, and it is best to keep spacing of 630 ft or less to achieve desirable crossing speeds of less or equal to 15 mph especially in a street with schools nearby. A microscopic simulation model was developed to replicate the driving behavior of traffic on urban road diets roads to analyze the influence of bus stops on traffic flow and safety. The impacts of safety were assessed using surrogate measures of safety (SSAM). The study found that presence of a bus stops for 10, 20 and 30 s dwell times have almost 9.5%, 12%, and 20% effect on traffic speed reductions when 300 veh/hr flow is considered. A comparison of reduction in speed of traffic on an 11 ft wide road lane of a road diet due to curbside stops and bus bays for a mean of 30s with a standard deviation of 5s dwell time case was conducted. Results showed that a bus stop bay with the stated bus dwell time causes an approximate 8% speed reduction to traffic at a flow level of about 1400 vph. Analysis of the trajectories from bust stop locations showed that at 0, 25, 50, 75, 100, 125, 150, and 175 feet from the intersection the number of conflicts is affected by the presence and location of a curbside stop on a segment with a road diet

Development of a hardware-in-the-loop analysis framework for advanced ITS applications

As Intelligent Transportation Systems (ITS) become more prevalent, there is a need for a system capable of the rigorous evaluation of new ITS strategies for a wide variety of applications. Pre-deployment testing and fine-tuning of the system, performance evaluation, and alternatives analysis are all potential benefits that could be gained through the evaluation of ITS. Simulation, an increasingly popular tool for transportation analysis, would seem an ideal solution to this problem as it allows for the consideration of many scenarios that may be improbable or impossible to observe in the field. Also, simulation provides a framework that allows for the application of rigorous analysis techniques to the output data, providing an accurate and statistically significant conclusion. The difficulty is that many ITS strategies are difficult or impossible to implement in a simulated environment. The rapid nature of technology development and the complicated nature of many ITS solutions are difficult to emulate in simulation models. Furthermore, the emulation of a particular ITS solution is not guaranteed to provide the same result that the physical system would, were it subject to the same inputs. This study seeks to establish a framework for the analysis of advanced ITS applications through the use of Hardware-in-the-Loop Simulation (HILS), which provides a procedure for interfacing simulation models with real-world hardware to conduct analysis. This solution provides the benefits of both advanced ITS evaluation and simulation for powerful and accurate analysis. A framework is established that includes all the steps of the modeling process including construction, validation, calibration, and output analysis. This ensures that the process surrounding the HILS implementation is valid so that the results of the evaluation are accurate and defendable. Finally, a case study of the application of the developed framework to the evaluation, a real-world implementation of an advanced ITS application (SCATS in this case) is considered. The effectiveness of the framework in creating and evaluating a corridor using a simulation model wed to real-world hardware is shown. The results of the analysis show the power of this method when correctly applied and demonstrate where further analysis could expand upon the proposed procedure.M.S.Committee Chair: Dr. Michael Hunter; Committee Member: Dr. Jiawen Yang; Committee Member: Dr. Jorge Laval; Committee Member: Dr. Michael Rodger

A Testing and Experimenting Environment for Microscopic Traffic Simulation Utilizing Virtual Reality and Augmented Reality

Microscopic traffic simulation (MTS) is the emulation of real-world traffic movements in a virtual environment with various traffic entities. Typically, the movements of the vehicles in MTS follow some predefined algorithms, e.g., car-following models, lane changing models, etc. Moreover, existing MTS models only provide a limited capability of two- and/or three-dimensional displays that often restrict the user’s viewpoint to a flat screen. Their downscaled scenes neither provide a realistic representation of the environment nor allow different users to simultaneously experience or interact with the simulation model from different perspectives. These limitations neither allow the traffic engineers to effectively disseminate their ideas to various stakeholders of different backgrounds nor allow the analysts to have realistic data about the vehicle or pedestrian movements. This dissertation intends to alleviate those issues by creating a framework and a prototype for a testing environment where MTS can have inputs from user-controlled vehicles and pedestrians to improve their traffic entity movement algorithms as well as have an immersive M3 (multi-mode, multi-perspective, multi-user) visualization of the simulation using Virtual Reality (VR) and Augmented Reality (AR) technologies. VR environments are created using highly realistic 3D models and environments. With modern game engines and hardware available on the market, these VR applications can provide a highly realistic and immersive experience for a user. Different experiments performed by real users in this study prove that utilizing VR technology for different traffic related experiments generated much more favorable results than the traditional displays. Moreover, using AR technologies for pedestrian studies is a novel approach that allows a user to walk in the real world and the simulation world at a one-to-one scale. This capability opens a whole new avenue of user experiment possibilities. On top of that, the in-environment communication chat system will allow researchers to perform different Advanced Driver Assistance System (ADAS) studies without ever needing to leave the simulation environment. Last but not least, the distributed nature of the framework enables users to participate from different geographic locations with their choice of display device (desktop, smartphone, VR, or AR). The prototype developed for this dissertation is readily available on a test webpage, and a user can easily download the prototype application without needing to install anything. The user also can run the remote MTS server and then connect their client application to the server

Real-Time Vehicle Emission Estimation Using Traffic Data

The current state of climate change should be addressed by all sectors that contribute to it. One of the major contributors is the transportation sector, which generates a quarter of greenhouse gas emissions in North America. Most of these transportation related emissions are from road vehicles; as result, how to manage and control traffic or vehicular emissions is therefore becoming a major concern for the governments, the public and the transportation authorities. One of the key requirements to emission management and control is the ability to quantify the magnitude of emissions by traffic of an existing or future network under specific road plans, designs and traffic management schemes. Unfortunately, vehicular traffic emissions are difficult to quantify or predict, which has led a significant number of efforts over the past decades to address this challenge. Three general methods have been proposed in literature. The first method is for determining the traffic emissions of an existing road network with the idea of measuring the tail-pipe emissions of individual vehicles directly. This approach, while most accurate, is costly and difficult to scale as it would require all vehicles being equipped with tail-pipe emission sensors. The second approach is applying ambient pollutant sensors to measure the emissions generated by the traffic near the sensors. This method is only approximate as the vehicle-generated emissions can easily be confounded by other nearby emitters and weather and environmental conditions. Note that both of these methods are measurement-based and can only be used to evaluate the existing conditions (e.g., after a traffic project is implemented), which means that it cannot be used for evaluating alternative transportation projects at the planning stage. The last method is model-based with the idea of developing models that can be used to estimate traffic emissions. The emission models in this method link the amount of emissions being generated by a group of vehicles to their operations details as well as other influencing factors such as weather, fuel and road geometry. This last method is the most scalable, both spatially and temporally, and also most flexible as it can meet the needs of both monitoring (using field data) and prediction. Typically, traffic emissions are modelled on a macroscopic scale based on the distance travelled by vehicles and their average speeds. However, for traffic management applications, a model of higher granularity would be preferred so that impacts of different traffic control schemes can be captured. Furthermore, recent advances in vehicle detection technology has significantly increased the spatiotemporal resolutions of traffic data. For example, video-based vehicle detection can provide more details about vehicle movements and vehicle types than previous methods like inductive loop detection. Using such detection data, the vehicle movements, referred to as trajectories, can be determined on a second-by-second basis. These vehicle trajectories can then be used to estimate the emissions produced by the vehicles. In this research, we have proposed a new approach that can be used to estimate traffic generated emissions in real time using high resolution traffic data. The essential component of the proposed emission estimation method is the process to reconstruct vehicle trajectories based on available data and some assumptions on the expected vehicle motions including cruising, acceleration and deceleration, and car-following. The reconstructed trajectories containing instantaneous speed and acceleration data are then used to estimate emissions using the MOVES emission simulator. Furthermore, a simplified rate-based module was developed to replace the MOVES software for direct emission calculation, leading to significant improvement in the computational efficiency of the proposed method. The proposed method was tested in a simulated environment using the well-known traffic simulator - Vissim. In the Vissim model, the traffic activities, signal timing, and vehicle detection were simulated and both the original vehicle trajectories and detection data recorded. To evaluate the proposed method, two sets of emission estimates are compared: the “ground truth” set of estimates comes from the originally simulated vehicle trajectories, and the set from trajectories reconstructed using the detection data. Results show that the performance of the proposed method depends on many factors, such as traffic volumes, the placement of detectors, and which greenhouse gas is being estimated. Sensitivity analyses were performed to see whether the proposed method is sufficiently sensitive to the impacts of traffic control schemes. The results from the sensitivity analyses indicate that the proposed method can capture impacts of signal timing changes and signal coordination but is insufficiently sensitive to speed limit changes. Further research is recommended to validate the proposed method using field studies. Another recommendation, which falls outside of this area of research, would be to investigate the feasibility of equipping vehicles with devices that can record their instantaneous fuel consumption and location data. With this information, traffic controllers would be better informed for emission estimation than they would be with only detection data

EVALUATING URBAN DOWNTOWN ONE-WAY TO TWO-WAY STREET CONVERSION USING MICROSCOPIC TRAFFIC SIMULATION

Located in the heart of Silicon Valley, Downtown San Jose is attracting new residents, visitors, and businesses. Clearly, the mobility of these residents, visitors, and businesses cannot be accommodated by streets that focus on the single-occupancy automobile mode. To increase the potential for individuals to use non-single-occupancy modes of travel, the downtown area must have a cohesive plan to integrate multimodal use and public life. Complete streets are an integral component of the multi-modal transport system and more livable communities. Complete streets refer to roads designed to accommodate multiple modes, users, and activities including walking, cycling, transit, automobile, and nearby businesses and residents. A one-way to two-way street conversion is an example of a complete streets project. Similarly, tactical urbanism can provide cost-effective modifications (e.g., through temporary road closures for events like the farmers’ market) that enrich the public life in an urban environment. The ability to serve current and future transportation needs of residents, businesses and visitors through the creation of pleasant, efficient, and safe multimodal corridors is a guiding principle of a smart city. This research project addressed questions that guide the implementation of this overarching principle. These questions relate to travel patterns and potential network impacts of the conversion of the corridor(s) into complete streets. Towards that end, core network in downtown San Jose is simulated via a validated VISSIM model for 2015 traffic conditions (i.e., the base case or Scenario 0). Three scenarios are then modeled as variations to this model. The relevant model outputs from the base and scenario models provide easily digestible information the City can convey various impacts and trade-offs to partners and stakeholders prior to implementation of these plans. The scenarios modeled are based on stakeholder input. Microsimulation allows for detailed modeling and visualization of the transportation networks including movements of individual vehicles and pedestrians. The results based on 2040 traffic volumes provided by the city based on their long-range travel demand model clearly demonstrate that the existing network cannot support the projected level of travel demand. It indicates that the city needs an aggressive travel demand management program to curb the growth of automobile traffic. The output also includes 3-D animations of the traffic flow that can be used in public forums for community outreach. A discussion for such a campaign based on best practices around using these visualizations for public outreach is also provided. Located in the heart of Silicon Valley, Downtown San Jose is attracting new residents, visitors, and businesses. Clearly, the mobility of these residents, visitors, and businesses cannot be accommodated by streets that focus on the single-occupancy automobile mode. To increase the potential for individuals to use non-single-occupancy modes of travel, the downtown area must have a cohesive plan to integrate multimodal use and public life. Complete streets are an integral component of the multi-modal transport system and more livable communities. Complete streets refer to roads designed to accommodate multiple modes, users, and activities including walking, cycling, transit, automobile, and nearby businesses and residents. A one-way to two-way street conversion is an example of a complete streets project. Similarly, tactical urbanism can provide cost-effective modifications (e.g., through temporary road closures for events like the farmers’ market) that enrich the public life in an urban environment. The ability to serve current and future transportation needs of residents, businesses and visitors through the creation of pleasant, efficient, and safe multimodal corridors is a guiding principle of a smart city. This research project addressed questions that guide the implementation of this overarching principle. These questions relate to travel patterns and potential network impacts of the conversion of the corridor(s) into complete streets. Towards that end, core network in downtown San Jose is simulated via a validated VISSIM model for 2015 traffic conditions (i.e., the base case or Scenario 0). A number o Threef scenarios are then modeled as variations to this model. The relevant model outputs from the base and scenario models provide easily digestible information the City can convey various impacts and trade-offs to partners and stakeholders prior to implementation of these plans. The scenarios modeled are based on stakeholder input. Microsimulation allows for detailed modeling and visualization of the transportation networks including movements of individual vehicles and pedestrians. The results based on 2040 traffic volumes provided by the city based on their long-range travel demand model clearly demonstrate that the existing network cannot support the projected level of travel demand. It indicates that the city needs an aggressive travel demand management program to curb the growth of automobile traffic. The output also includes 3-D animations of the traffic flow that can be used in public forums for community outreach. A discussion for such a campaign based on best practices around using these visualizations for public outreach is also provided

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness