Real-Time Vehicle Route Guidance Based on Connected Vehicles

2013 ◽  
Author(s):  
Daxin Tian ◽  
Yong Yuan ◽  
Jianshan Zhou ◽  
Yunpeng Wang ◽  
Guangquan Lu ◽  
...  
Keyword(s):  
Real Time ◽  
Connected Vehicles ◽  
Route Guidance ◽  
Vehicle Route

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals Network Calculus-Based Latency for Time-Triggered Traffic under Flexible Window-Overlapping Scheduling (FWOS) in a Time-Sensitive Network (TSN)

2021 ◽  
Vol 11 (9) ◽  
pp. 3896
Author(s):  
Khaled M. Shalghum ◽  
Nor Kamariah Noordin ◽  
Aduwati Sali ◽  
Fazirulhisyam Hashim
Keyword(s):  
Real Time ◽  
Connected Vehicles ◽  
Network Calculus ◽  
Timing Constraints ◽  
Traffic Scheduling ◽  
Worst Case ◽  
Continuous Increase ◽  
Scheduled Traffic ◽  
Support Time ◽  
New Framework

Deterministic latency is an urgent demand to pursue the continuous increase in intelligence in several real-time applications, such as connected vehicles and automation industries. A time-sensitive network (TSN) is a new framework introduced to serve these applications. Several functions are defined in the TSN standard to support time-triggered (TT) requirements, such as IEEE 802.1Qbv and IEEE 802.1Qbu for traffic scheduling and preemption mechanisms, respectively. However, implementing strict timing constraints to support scheduled traffic can miss the needs of unscheduled real-time flows. Accordingly, more relaxed scheduling algorithms are required. In this paper, we introduce the flexible window-overlapping scheduling (FWOS) algorithm that optimizes the overlapping among TT windows by three different metrics: the priority of overlapping, the position of overlapping, and the overlapping ratio (OR). An analytical model for the worst-case end-to-end delay (WCD) is derived using the network calculus (NC) approach considering the relative relationships between window offsets for consecutive nodes and evaluated under a realistic vehicle use case. While guaranteeing latency deadline for TT traffic, the FWOS algorithm defines the maximum allowable OR that maximizes the bandwidth available for unscheduled transmission. Even under a non-overlapping scenario, less pessimistic latency bounds have been obtained using FWOS than the latest related works.

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

scholarly journals Real-time urban regional route planning model for connected vehicles based on V2X communication

2020 ◽  
Vol 13 (1) ◽  
pp. 517-538 ◽  
Author(s):  
Pangwei Wang ◽  
Hui Deng ◽  
Juan Zhang ◽  
Mingfang Zhang
Keyword(s):  
Land Use ◽  
Travel Time ◽  
Real Time ◽  
Land Use Planning ◽  
Route Planning ◽  
Urban Traffic ◽  
Urban Transport ◽  
Connected Vehicles ◽  
Planning Model ◽  
Optimal Route

Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. First, considering the traffic signal timing and phase information collected by V2X, road section resistance values are calculated dynamically based on real-time vehicular driving data. Second, according to the topology structure of the current regional road network, all predicted routes are listed based on the Dijkstra algorithm. Third, the predicted travel time of each alternative route is calculated, while the predicted route with the least travel time is selected as the optimal route. Finally, we design the test scenario with different traffic saturation levels and collect 150 sets of data to analyze the feasibility of the proposed method. The numerical results have shown that the average travel times calculated by the proposed optimal route are 8.97 seconds, 12.54 seconds, and 21.85 seconds, which are much shorter than the results of traditional navigation routes. This proposed model can be further applied to the whole urban traffic network and contribute to a greater transport and land-use efficiency in the future.

Sign in / Sign up

Export Citation Format

Share Document