scholarly journals Real-Time Implementation of Green Light Optimal Speed Advisory for Electric Vehicles

Vehicles ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 35-54 ◽  
Author(s):  
Lior Simchon ◽  
Raul Rabinovici
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Optimization Problem ◽  
Green Light ◽  
Optimal Solution ◽  
Transportation Systems ◽  
Traffic Light ◽  
Calculation Time ◽  
Optimal Speed

Intelligent Transportation Systems (ITS), such as Green Light Optimal Speed Advisory (GLOSA) systems, can be used to reduce the energy consumption in modern vehicles. In particular, GLOSA systems provide driving strategies that can decrease both energy consumption and travel time. In this paper, we present a new method to calculate the optimal driving speeds based on traffic light data. To this end, a detailed formulation for the optimization problem is presented for a multi-segment route, based on an electric vehicle (EV) and traffic light models in an urban environment. Since this formulation results in a nonconvex optimization problem, a relaxation procedure is applied with a low calculation time. By using this procedure, a dynamic real-time speed advisory algorithm is developed. Numerical simulations showed improved performance over benchmark techniques. In particular, the proposed Dynamic-GLOSA solution’s performance was shown to be very close to that with a brute-force optimal solution but with a much shorter calculation time and has significant potential for energy saving.

Traffic Signal Control for a Single Intersection-Based Intelligent Transportation System

2020 ◽  
pp. 159-180
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Abderrahim Hasbi
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
The Road ◽  
Real Time Traffic

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

scholarly journals Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 127 ◽  
Author(s):  
Mingbin Zeng ◽  
Xu Yang ◽  
Mengxing Wang ◽  
Bangjiang Xu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Optimal Solution ◽  
Transportation Systems ◽  
Function Optimization ◽  
Dynamic Path Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

scholarly journals A New Approach for Real Time Train Energy Efficiency Optimization

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2660 ◽  
Author(s):  
Agostinho Rocha ◽  
Armando Araújo ◽  
Adriano Carvalho ◽  
João Sepulveda
Keyword(s):  
Energy Efficiency ◽  
Simulated Annealing ◽  
Energy Consumption ◽  
Real Time ◽  
Optimization Algorithm ◽  
Normal Operation ◽  
Speed Profile ◽  
Railway Systems ◽  
Optimal Speed ◽  
Advisory System

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

scholarly journals Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

2020 ◽  
Vol 14 (02) ◽  
pp. 196 ◽  
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Aberrahim Hasbi
Keyword(s):  
Road Traffic ◽  
Green Light ◽  
Optimal Solution ◽  
Ant Colony ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

scholarly journals Real-Time Adaptive Traffic Signals Using Rate-Monotonic Scheduling

2019 ◽  
Vol 29 ◽  
pp. 03002 ◽  
Author(s):  
Mãdãlin-Dorin Pop
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Fixed Time ◽  
Transportation Systems ◽  
Real Time Scheduling ◽  
Traffic Lights ◽  
Rate Monotonic ◽  
Rate Monotonic Scheduling

The studies and real situations shown that the traffic congestion is one of nowadays highest problems. This problem wassolved in the past using roundabouts and traffic signals. Taking in account the number of cars that is increasing continuously, we can see that past approaches using traffic lights with fixed-time controller for traffic signals timing is obsolete. The present and the future is the using of Intelligent Transportation Systems. Traffic lights systems should be aware about realtime traffic parameters and should adapt accordingly to them. The purpose of this paper is to present a new approach to control traffic signals using rate-monotonic scheduling. Obtained results will be compared with the results obtained by using others real-time scheduling algorithms.

Impact of Travel Time Models on Quality of Real-Time Routing Instructions

2003 ◽  
Vol 1857 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Elise Miller-Hooks ◽  
Baiyu Yang
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Communication Systems ◽  
Service Providers ◽  
A Priori ◽  
Information Service ◽  
Small Neighborhood ◽  
Transportation Systems ◽  
Travel Times ◽  
Real Time Information

Mobile communication systems coupled with intelligent transportation systems technologies can permit information service providers to supply real-time routing instructions to suitably equipped vehicles as real-time travel times are received. Simply considering current conditions in updating routing decisions, however, may lead to suboptimal path choices, because future travel conditions likely will differ from that currently observed. Even with perfect and continuously updated information about current conditions, future travel times can be known a priori with uncertainty at best. Further, in congested transportation systems, conditions vary over time as recurrent congestion may change with a foreseeable pattern during peak driving hours. It is postulated that better, more robust routing instructions can be provided by explicitly accounting for this inherent stochastic and dynamic nature of future travel conditions in generating the routing instructions. It is further hypothesized that nearly equally good routing instructions can be provided by collecting real-time information from only a small neighborhood within the transportation system as from the entire system. Extensive numerical experiments were conducted to assess the validity of these two hypotheses.

Detection of Real-Time Event in Intelligent Traffic System Based on RFID

2014 ◽  
Vol 926-930 ◽  
pp. 1314-1317 ◽  
Author(s):  
Li Yang
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Engine Model ◽  
Intelligent Traffic System ◽  
Complex Events ◽  
Rules Engine

To solve the demand of real-time event detection in the RFID-based Intelligent Transportation Systems , using Complex Event Processing technology to establish a rule model to detect events.The model allows users to customize the Basic Events and Complex Events, using the rule files describe the complex events modes, clearly expressed the timing and gradation relationships between RFID events, meeting the needs of real-time event detection in the Intelligent Transportation System ,achieving the appropriate rules engine,. Finally, test and verify the effectiveness of the rules file and the rules engine model by experiments.

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