Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

Abstract

This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future.

Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes

“…Future research will focus on developing more data-efficient algorithms for real-world implementation. Additionally, another study [14] addresses sustainability challenges in transportation networks, specifically focusing on conflicts between trams and Connected Vehicles (CVs) at intersections. They propose a Transit Signal Priority (TSP) system aimed at reducing delays, minimizing energy consumption, and enhancing passenger comfort.…”

“…This research plays a vital role in promoting sustainable urban mobility by optimizing transportation systems for efficiency, reduced emissions, and improved passenger experience. Moreover, this research draws inspiration from recent studies that aim to enhance road safety while promoting sustainability in transportation networks [14]. The proposed framework is designed to minimize delays and energy consumption, further contributing to the sustainability of urban mobility.…”

Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

“…In the past two decades, autonomous driving has emerged as a key innovation in the realm of sustainable smart cities, attracting substantial research interest due to its potential societal and economic benefits. Autonomous vehicles (AVs) are poised to revolutionize urban transportation systems by enhancing efficiency and safety, and reducing environmental impacts [1,2]. Central to the vision of sustainable smart cities, AVs navigate intricate and dynamic traffic environments, a significant challenge for their decision-making capabilities.…”