Enhanced speed advice for connected vehicles in the proximity of signalized intersections

Abstract

Technological advancements in the field of transportation are gradually enabling cooperative, connected and automated mobility (CCAM). The progress in information and communication technology (ICT) has provided mature solutions for infrastructure-to-vehicle (I2V) communication, which enables the deployment of Cooperative-ITS (C-ITS) services that can foster comfortable, safe, environmentally friendly, and more efficient traffic operations. This study focuses on the enhancement of speed advice comfort and safety in the proximity of signalized intersections, while ensuring energy and traffic efficiency. A detailed microscopic simulation model of an urban network in the city of Thessaloniki, Greece is used as test bed. The performance of dynamic eco-driving is evaluated for different penetration rates of the dynamic eco-driving technology and varying traffic conditions. The simulation analysis indicates that speed advice can be comfortable and safe without adversely impacting energy and traffic efficiency. However, efficient deployment of dynamic eco-driving depends on road design characteristics, activation distance of the service, traffic signal plans, and prevailing traffic conditions.

Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies

“…Table 3 together with Figure 7 and Figure 8 reveal that during the start of the test measurements, there were more packet losses which ultimately led to an increase in network latency. However, as a whole, the LTE and 5GTN performances were satisfactory for fulfilling the minimum requirements to deliver the safety messages in the V2V and V2I scenarios (<100 ms) [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

“…To implement this arrangement, the VANET will require authorization to gain access to the cyberspace via existing LTE infrastructure, and this heterogenous system will increase the vehicular communication system’s efficiency. Nonetheless, mobile nodes in large numbers appear as a challenging aspect in this system [ 16 , 17 , 18 , 19 , 20 ]. Hence, the field measurements that were performed and studied in this paper involve a combination of an improved clustering framework, called multi-parallel processing, with multiband OFDM (MP-MBOFDM).…”

“…The 5GTN is basically deployed for the design and development of 5G applications and services related to vehicular communication. It uses 3.5 GHz by supporting different data rates, varying from 10 to 50 Mbps in the spectrum of 60 MHz in the downlink and uplink correspondingly [ 18 , 19 , 20 ]. Theoretically, this cellular network may not be the best choice for vehicular communication, but it aids in the development of 5G services and applications and helps to determine issues in current cellular systems.…”

Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach

“…Given this, the integration of SPaT information and advanced control policies initiate eco-driving concept, which has potential to significantly reduce energy consumption in the proximity of signalized intersections (Li et al, 2018). Generally, the control policies take vehicle location, SPaT data, and local traffic state as input and generate speed profile for the controlled vehicle (i.e., the ego vehicle), and thus en-hance the performance of the vehicle in terms of energy conservation (Asadi and Vahidi, 2011;Mintsis et al, 2021;Dong et al, 2021;Ma et al, 2021).…”

An Intersection Platoon Speed Control Model Considering Traffic Efficiency and Energy Consumption in CVIS

“…In the research of speed control, Lee and Li [16] proposed an eco-driving advisory system (EDAS) that reduces CO 2 emissions and energy consumption by letting the vehicle continuously pass through multiple intersections with the minimum possibilities of stops. Mintsis et al [17] used a detailed microscopic simulation model of a city network in essaloniki, Greece, as a test bed to evaluate the performance of dynamic eco-driving for different penetration rates of the dynamic eco-driving technology and varying traffic conditions. Yang et al [18] conducted real-world vehicle driving measurements to directly study the effectiveness of SG-ITS in terms of driving behaviour, time savings, fuel consumption, and pollutant emissions by driving vehicles on the same routes with and without SG-ITS speed guidance.…”