Dynamic Vehicular Trajectory Optimization for Bottleneck Mitigation and Safety Improvement

Traffic bottleneck is defined as a disruption of traffic flow through a freeway or an arterial, which can be divided as two categories: stationary bottleneck and moving bottleneck. The stationary bottleneck is mainly formed by the lane drops in the multi-lane roadways, while the moving bottleneck are due to the very slowing moving vehicles which disrupt the traffic flow. Traffic bottlenecks not only impact the mobility, but also potentially cause safety issues. Traditional strategies for eliminating bottlenecks mainly focus on expanding supply including road widening, green interval lengthening and optimization of intersection channelization. In addition, a few macroscopic methods are also made to optimize the traffic demand such as routing optimization, but these studies have some drawbacks due to the limitations of times and methodologies. Therefore, this research utilizes the Connected and Autonomous Vehicles (CAV) technology to develop several cooperative trajectory optimization models for mitigating mobility and safety impact caused by the urban bottlenecks. The multi-phases algorithms is developed to help solve the model, where a multi-stage-based nonlinear programming procedure is developed in the first phase to search trajectories that eliminate the conflicts in the bottleneck and minimize the travel time and the remaining ones refine the trajectories with a mixed integer linear programming to minimize idling time of vehicles, so that fuel consumption and emissions can be lowered down. Sensitivity analyses are also conducted towards those models and they imply that several indices may significantly impact the effectiveness and even cause the models lose efficacy under extreme values. Various illustrative examples and sensitivity analyses are provided to validate the proposed models. Results indicate that (a) the model is effective to mitigate the mobility and safety impact of bottleneck under the appropriate environment; (b) the model could simultaneously optimize the trajectories of vehicles to lower down fuel consumption and emissions; (c) Some environment indices may significantly impact the models, and even cause the model to lose efficacy under extreme values. Application of the developed models under a real-world case illustrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating the intelligent transportation management

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

Public transport trajectory planning with probabilistic guarantees

The paper proposes an eco-cruise control strategy for urban public transportbuses. The aim of the velocity control is ensuring timetable adherence, whileconsidering upstream queue lengths at traffic lights in a probabilistic way. Thecontribution of the paper is twofold. First, the shockwave profile model (SPM)is extended to capture the stochastic nature of traffic queue lengths. The modelis adequate to describe frequent traffic state interruptions at signalized intersections.Based on the distribution function of stochastic traffic volume demand,the randomness in queue length, wave fronts, and vehicle numbers are derived.Then, an outlook is provided on its applicability as a full-scale urban traffic networkmodel. Second, a shrinking horizon model predictive controller (MPC) isproposed for ensuring timetable reliability. The intention is to calculate optimalvelocity commands based on the current position and desired arrival time of thebus while considering upcoming delays due to red signals and eventual queues.The above proposed stochastic traffic model is incorporated in a rolling horizonoptimization via chance-constraining. In the optimization, probabilistic guaranteesare formulated to minimize delay due to standstill in queues at signalized intersections. Optimization results are analyzed from two particular aspects, (i)feasibility and (ii) closed-loop performance point of views. The novel stochasticprofile model is tested in a high fidelity traffic simulator context. Comparativesimulation results show the viability and importance of stochastic bounds in urbantrajectory design. The proposed algorithm yields smoother bus trajectoriesat an urban corridor, suggesting energy savings compared to benchmark controlstrategies

Hardware-in-the-Loop Simulation to Evaluate the Performance and Constraints of the Red-light Violation Warning Application on Arterial Roads

Understanding the safety and mobility impacts of Connected Vehicle (CV) applications is critical for ensuring effective implementations of these applications. This dissertation provides an assessment of the safety and mobility impacts of the Red-Light Violation Warning (RLVW), a CV-based application at signalized intersections, under pre-timed signal control and semi-actuated signal control utilizing Emulator-in-the-loop (EILS), Software-in-the-loop (SILS), and Hardware-in-the-loop simulation (HILS) environments. Modern actuated traffic signal controllers contain several features with which controllers can provide varying green intervals for actuated phases, skip phases, and terminate phases depending on the traffic demand fluctuation from cycle to cycle. With actuated traffic signal operations, there is uncertainty in the end-of-green information provided to the vehicles using CV messages. The RLVW application lacks accurate input information about when exactly a phase is going to be terminated since this termination occurs when a gap of a particular length is encountered at the detector. This study compares the results obtained with the use of these three aforementioned simulation platforms and how the use of the platforms impacts the assessed performance of the modeled CV application. In addition, the study investigates using HILS and a method to provide an Assured Green Period (AGP) which is a definitive time when the green interval will end to mitigate the uncertainties associated with the green termination and to improve the performance of the CV application. The study results showed that in the case of pre-timed signal control, there are small differences in the assessed performance when using the three simulated platforms. However, in the case of the actuated control, the utilization of EILS showed significantly different results compared to the utilization of the SILS and the HILS platforms. The use of the SILS and the HILS platforms produced similar results. The differences can be attributed to the variations in the time lag between vehicle detection and the use of this information between the EILS and the other two platforms. In addition, the results showed that the reduction in red-light running due to RLVW was significantly higher with pre-timed control compared to the reduction with semi-actuated control. The reason is the uncertainty in the end-of-green intervals provided in the messages communicated to the vehicles, as stated above. In the case of semi-actuated control, the results showed that the safety benefits of the RLVW without the use of AGP were limited. On the other hand, the study results showed that by introducing the AGP, the RLVW can reduce the number of red-light running events at signalized intersections by approximately 92% with RLVW utilization of 100%. However, the results show that the application of the AGP, as applied and assessed in this dissertation, can have increased stopped delay and approach delay under congested traffic conditions. This issue will need to be further investigated to determine the optimal setting of the AGP considering both mobility and safety impacts

Measuring the Quality of Arterial Traffic Signal Timing – A Trajectory-based Methodology

Evaluating the benefits from traffic signal timing is of increasing interest to transportation policymakers, operators, and the public as integrating performance measurements with agencies’ daily signal timing management has become a top priority. This dissertation presents a trajectory-based methodology for evaluating the quality of arterial signal timing, a critical part of signal operations that promises reduced travel time and fewer vehicle stops along arterials as well as improved travelers’ perception of transportation services. The proposed methodology could significantly contribute to performance-oriented signal timing practices by addressing challenges regarding which performance measures should be selected, how performance measurements can be performed cost-effectively, and how to make performance measures accessible to people with limited knowledge of traffic engineering. A review of the current state of practice and research was conducted first, indicating an urgent research need for developing an arterial-level methodology for signal timing performance assessments as the established techniques are mostly based on by-link or by-movement metrics. The literature review also revealed deficiencies of existing performance measures pertaining to traffic signal timing. Accordingly, travel-run speed and stop characteristics, which can be extracted from vehicle GPS trajectories, were selected to measure the quality of arterial signal timing in this research.Two performance measures were then defined based on speed and stop characteristics: the attainability of ideal progression (AIP) and the attainability of user satisfaction (AUS). In order to determine AIP and AUS, a series of investigations and surveys were conducted to characterize the effects of non-signal-timing-related factors (e.g., arterial congestion level) on average travel speed as well as how stops may affect travelers’ perceived quality of signal timing. AIP was calculated considering the effects of non-signal-timing-related factors, and AUS accounted for the changes in the perceived quality of signal timing due to various stop circumstances.Based upon AIP and AUS, a grade-based performance measurement methodology was developed. The methodology included AIP scoring, AUS scoring, and two scoring adjustments. The two types of scoring adjustments further improved the performance measurement results considering factors such as cross-street delay, pedestrian delays, and arterial geometry. Furthermore, the research outlined the process for implementing the proposed methodology, including the necessary data collection and the preliminary examination of the applicable conditions. Case studies based on real-world signal re-timing projects were presented to demonstrate the effectiveness of the proposed methodology in enhancing agencies’ capabilities of cost-effectively monitoring the quality of arterial signal timing, actively addressing signal timing issues, and reporting the progress and outcomes in a concise and intuitive manner

Utilizing shockwave theory and deep learning to estimate intersection traffic flow and queue length based on connected vehicle data.

The development of Connected Vehicles (CV) facilitates the use of trajectory data to estimate queue length and traffic volume at signalized intersections. The previously proposed models involved additional information that may require conducting different types of data collection. Also, most models need higher market penetration rate or more than a vehicle per cycle to provide adequate estimation. This is mainly because a significant number of the estimation models utilized only queued vehicles. However, the state of motion in non-queued vehicles, particularly slowed-down vehicles, provides much information about the traffic characteristics. There is a lack of exploiting the information from slowed-down vehicles in identifying the last queued vehicle to improve the estimation models. The importance of this work is to propose a cycle-by-cycle queue length and traffic volume estimation models by utilizing the slowed-down vehicles. It proposes a sophisticated model to estimate the queue length and traffic volume from connected vehicles with low market penetration rate (MPR) by utilizing shockwave theory and deep learning technique (artificial neural network). The work starts with establishing a relationship between the slowed-down vehicles and last queued vehicles based on shockwave theory and traffic flow dynamics. Then, the queue estimation algorithm is modeled based on the capacity state and deep learning technique. The traffic volume algorithm modeled is based on the queue length information. Four experiments were conducted to investigate the performance of the queue length and traffic volume estimation models on dataset from simulation environment and real-world data. The queue length results of the simulation experiment demonstrated an adequate mean absolute percentage error (MAPE) of 13.44%, which means an accuracy of 86.56%. The highest MAPE was 19.16% (80.84% accuracy) and the lowest was 7.36% (92.64%). The results of the queue length algorithm applied on real-world data demonstrated an MAPE of 21.97% (78.03% accuracy). The performance of the traffic volume algorithm on simulation data demonstrated an excellent MAPE of 11.8% (88.2% accuracy). The performance of the algorithm based on real-world data from demonstrated an MAPE of 23.57% (76.43% accuracy). Although the previous models can provide similar accuracy rates, they require higher MPR. With the low MPR of 10%, the proposed models revealed an adequate estimation accuracy of the cycle-by-cycle queue length and traffic volume

Optimisation of Signal Timing at Intersections with Waiting Areas

Unconventional geometric designs such as continu-ous-flow intersections, U-turns, and contraflow left-turn lanes have been proposed to reduce left-turn conflicts and improve intersection efficiency. Having a waiting area at a signalised intersection is an unconventional de-sign that is used widely in China and Japan to improve traffic capacity. Many studies have shown that waiting areas improve traffic capacity greatly, but few have con-sidered how to improve the benefits of this design from the aspect of signal optimisation. Comparing the start-up process of intersections with and without waiting areas, this work explores how this geometric design influenc-es vehicle transit time, proposes two signal optimisation strategies, and establishes a unified capacity calculation model. Taking capacity maximisation as the optimisation function, a cycle optimisation model is derived for over-saturated intersections. Finally, the relationship among waiting-area storage capacity, cycle time, and traffic ca-pacity is discussed using field survey data. The results of two cases show that optimising the signal scheme helps reduce intersection delays by 10–15%