Field implementation feasibility study of cumulative travel-time responsive (CTR) traffic signal control algorithm

The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50-60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. © 2017 John Wiley & Sons, Ltd.1

Transit Signal Priority with Connected Vehicle Technology

Transit signal priority (TSP) has been studied as a control strategy that offers preference to transit vehicles at signalized intersections. Although TSP has been deployed in many places, several shortcomings, such as adverse effect on side streets and uncertainty about the benefit, have been identified. Therefore, a new TSP logic proposed to overcome these shortcomings takes advantage of the resources provided by connected vehicle technology, including two-way communications between buses and the traffic signal controller, accurate bus location detection and prediction, and number of passengers. The key feature of the proposed TSP logic is green time reallocation, which moves green time instead of adding extra green time. TSP is also designed to be conditional. That is, delay per person is used as the most important criterion in deciding whether TSP is to be granted. The logic developed in this research was evaluated in two ways: with analytical and microscopic simulation approaches. In each evaluation, the proposed TSP was compared with two scenarios: no TSP and conventional TSP. The analysis used bus delay and per person delay of all travelers as measures of effectiveness. The simulation-based evaluation results showed that the proposed TSP logic reduced bus delay between 9% and 84% compared with conventional TSP and between 36% and 88% compared with the no-TSP condition. The range of improvement corresponding to four volume-to-capacity ratios was tested. No significant negative effects were caused by the proposed TSP logic

Transit signal priority accommodating conflicting requests under Connected Vehicles technology

In this research, a person-delay-based optimization method is proposed for an intelligent Transit Signal Priority (TSP) logic that resolves multiple conflicting TSP requests at an isolated intersection. This TSP with Connected Vehicles accommodating Conflicting Requests (TSPCV-CR) overcomes the challenge bore by the conventional “first come first serve” strategy and presents significant improvement on bus service performance. The feature of TSPCV-CR includes green time re-allocation, simultaneous multiple buses accommodation, and signal-transit coordination. These features help maximize the transit TSP service rate and minimize adverse effect on competing travel directions. The TSPCV-CR is also designed to be conditional. That is, TSP is granted only when the bus is behind schedule and the grant of TSP causes no extra total person delay. The optimization is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound routine. Minimizing per person delay is the objective of the optimization model. The logic developed in this research is evaluated using both analytical and microscopic traffic simulation approaches. Both analytical tests and simulation evaluations compared three scenarios: without TSP (NTSP), conventional TSP (CTSP), and TSP with Connected Vehicles that resolves Conflicting Requests (TSPCV-CR). The measures of effectiveness used include bus delay and total travel time of all travelers. The performance of TSPCV-CR is compared against conventional TSP (CTSP) under four congestion levels and three different conflicting scenarios. The results show that the TSPCV-CR greatly reduces bus delay at signalized intersection for all congestion levels and conflicting scenarios considered. Simulation based evaluation results show that the TSPCV-CR logic reduces average bus delay between 5% and 48% compared to the conventional TSP. The range of improvement corresponding to the four different v/cratios tested, which are 0.5, 0.7, 0.9 and 1.0, respectively. No statistically significant negative effects are observed

Coordinated transit signal priority supporting transit progression under Connected Vehicle Technology

In this paper, a person-delay-based optimization method is proposed for an intelligent TSP logic that enables bus/signal cooperation and coordination among consecutive signals under the Connected Vehicle environment. This TSP logic, called TSPCV-C, provides a method to secure the mobility benefit generated by the intelligent TSP logic along a corridor so that the bus delay saved at an upstream intersection is not wasted at downstream intersections. The problem is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound method. Minimizing per person delay has been adopted as the criterion for the model. The TSPCV-C is also designed to be conditional. That is, TSP is granted only when the bus is behind schedule and the grant of TSP causes no extra total person delay. The logic developed in this research is evaluated using both analytical and microscopic traffic simulation approaches. Both analytical tests and simulation evaluations compared four scenarios: without TSP (NTSP), conventional TSP (CTSP), TSP with Connected Vehicle (TSPCV), and Coordinated TSP with Connected Vehicle (TSPCV-C). The measures of effectiveness used include bus delay and total travel time of all travelers. The performance of TSPCV-C is compared against conventional TSP (CTSP) under four congestion levels and five intersection spacing cases. The results show that the TSPCV-C greatly reduces bus delay at signalized intersection for all congestion levels and spacing cases considered. Although the TSPCV is not as efficient as TSPCV-C, it still demonstrates sizable improvement over CTSP. An analysis on the intersection spacing cases reveals that, as long as the intersections are not too closely spaced, TSPCV can produce a delay reduction up to 59%. Nevertheless, the mechanism of TSPCV-C is recommended for intersections that are spaced less than 0.5 mile away. Simulation based evaluation results show that the TSPCV-C logic reduces the bus delay between 55% and 75% compared to the conventional TSP. The range of improvement corresponding to the four different v/c ratios tested, which are 0.5, 0.7, 0.9 and 1.0, respectively. No statistically significant negative effects are observed except when the v/c ratio equals 1.0

Directions for next generation microscopic traffic simulation modeling tool under the IntelliDrive environment

Microscopic traffic simulation models have been widely accepted in the evaluations of new treatments in the surface transportation system. These include new highway, lane usage (e.g., high occupancy vehicle lane or high occupancy toll lane), speed limits (e.g., variable speed limit, and uniform or differential speed limits), etc. Additional needs such as considering lateral movements within the lane made researchers develop plug-in modules on the basis of application programming interface (API). With a recent initiation of IntelliDrive or cooperative vehicle infrastructure system, a traffic simulation research community has faced to consider directions for the future microscopic traffic simulation modeling tools.\ud This paper conducted comprehensive assessments on the existing practices in the microscopic simulation modeling and future modeling needs, and recommended that the development of traffic simulation model independent plug-in modules. Additional recommendations including short-term and long-term approaches were\ud discussed

Connected vehicle data for crash hotspots

Identifying crash hotspots help traffic engineers implementing countermeasures to improve transportation system safety. However, given it typically takes 12 to 18 months to obtain crash reports, the crash hotspots based on actual crash reports might not be applicable (as the hotspot locations often change). In order to identify hotspots more timely than current practice, we have investigated the basic safety message (BSM) including equipped vehicle deceleration data at every 0.1 seconds from connected vehicles. Based on the safety pilot study data from April 2013, we proposed “extreme braking event” as a crash surrogate. The extreme braking event was defined as a longitudinal deceleration rate greater than 0.6 g (or 19.32 ft/s2). When comparison was made between the actual crash (i.e., crash rate) and the extreme braking event (i.e., event rate based on the number of equipped vehicles) at the same freeway segments in the city of Ann Arbor, the proposed extreme braking event showed high correlation (over 0.7). In addition, top 5 actual crash hotspots (about 50% of crashes out of 38 segments) were identified by using top 8 extreme braking event measures. Thus, our research indicates that the proposed extreme braking event measure has of great potential to identify crash hotspots in timely manner than the current practice based on delayed crash reports

Speed harmonisation and merge control using connected automated vehicles on a highway lane closure: a reinforcement learning approach

A lane closure bottleneck usually leads to traffic congestion and a waste of fuel consumption on highways. In mixed traffic that consists of human-driven vehicles and connected automated vehicles (CAVs), the CAVs can be used for traffic control to improve the traffic flow. The authors propose speed harmonisation and merge control, taking advantage of CAVs to alleviate traffic congestion at a highway bottleneck area. To this end, they apply a reinforcement learning algorithm called deep Q network to train behaviours of CAVs. By training the merge control Q-network, CAVs learn a merge mechanism to improve the mixed traffic flow at the bottleneck area. Similarly, speed harmonisation Q-network learns speed harmonisation to reduce fuel consumption and alleviate traffic congestion by controlling the speed of following vehicles. After training two Q-networks of the merge mechanism and speed harmonisation, they evaluate the trained Q-networks under various conditions in terms of vehicle arrival rates and CAV market penetration rates. The simulation results indicate that the proposed approach improves the mixed traffic flow by increasing the throughput up to 30% and reducing the fuel consumption up to 20%, when compared to the late merge control without speed harmonisation. © 2020 The Institution of Engineering and Technology.FALS

Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS