The Influence of Intersections on Fuel Consumption in Urban Arterial Road Traffic: A Single Vehicle Test in Harbin, China

Wu, Ling; Ci, Yusheng; Chu, Jiangwei; Zhang, Hongsheng

doi:10.1371/journal.pone.0137477

Cited by 23 publications

(11 citation statements)

References 22 publications

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“…It is observed that vehicles have lower MPG near the intersections where drivers often make sudden and frequent stops, and higher MPG in the middle of roads where vehicles tend to coast and brake lightly. The results agree with a study conducted in the urban arterial roads in Harbin, China which reported that 50.36% of the fuel was spent near intersections whose area only covers 28.9% of the total distance traveled [25]. Indeed, the results motivate a number of eco-driving techniques including eco-driving at signalized intersections, which aims to reduce the fuel consumption by minimizing hard braking and acceleration.…”

Section: A Influence Of Driving Environments On Fuel Economysupporting

confidence: 87%

Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research

LeBlanc

Peng

2019

Preprint

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We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their timeseries data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybridelectric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include datadriven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED.

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Section: A Influence Of Driving Environments On Fuel Economysupporting

confidence: 87%

Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research

LeBlanc

Peng

2019

Preprint

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“…where K is a constant term related only to the initial speed, p k j is the traction power at iteration j time k, fc k j is the fuel consumption rate at iteration j time k, a is the vector form of the acceleration in the planning horizon, D is a N×N lower triangle matrix representing the kinematic model (12) The assumption here is that in the trust region, the fuel consumption and the speed of the last iteration are valid approximations of the true value. The trust region method would impose additional linear constraint on speed and acceleration ( 1) where ρ v and ρ a are the trust region radius of speed and acceleration respectively. It is also noted from the solver that since breach-and-bound is used to solve the mixed-integer problem, the application of the trust region at each iteration would reduce the size of the search tree.…”

Section: A Mixed-integer Problem Formulationmentioning

confidence: 99%

“…In city driving, stop-and-go and idling due to congestion and signalized intersections cause significantly increased fuel consumption. A Recent study [1] shows that on average fuel consumption at signalized intersections accounts for more than 50% of the total consumption for a whole trip. The main techniques used to address this issue are adaptive traffic signal controls such as SCOOT [2] and SCATS [3].…”

Section: Introductionmentioning

confidence: 99%

Speed trajectory planning at signalized intersections using sequential convex optimization

Huang

Peng

2017

2017 American Control Conference (ACC)

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An algorithm is developed to optimize vehicle speed trajectory over multiple signalized intersections with known traffic signal information to minimize fuel consumption and travel time, and to meet ride comfort requirements using sequential convex optimization method. A comparison between the proposed method and dynamic programming is carried out to verify its optimality. In addition, vehicle motion during turning is studied because of its significant effect on fuel consumption and travel time.

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“…A promising and practical solutions to this problem is to make road traffic as efficiently as possible. In particular, since intersections represent one of the major bottlenecks of traffic flow [1], optimizing intersection management is currently highly important to improve traffic efficiency and safety.…”

Section: Introductionmentioning

confidence: 99%

Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning

Yan¹,

Welschehold²,

Büscher³

et al. 2021

Preprint

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The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven ones and the whole traffic system in different ways, for example by improving collision avoidance and reducing traffic waves. Many studies have been carried out to improve intersection management, a significant bottleneck in traffic, with intelligent traffic signals or exclusively automated vehicles. However, the problem of how to improve mixed traffic at unsignalized intersections has received less attention. In this paper, we propose a novel approach to optimizing traffic flow at intersections in mixed traffic situations using deep reinforcement learning. Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow. We implemented our approach and tested it in the traffic simulator SUMO based on simulated and real traffic data. The experimental evaluation demonstrates that our method significantly improves traffic flow through unsignalized intersections in mixed traffic settings and also provides better performance on a wide range of traffic situations compared to the state-of-the-art traffic signal controller for the corresponding signalized intersection.

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The Influence of Intersections on Fuel Consumption in Urban Arterial Road Traffic: A Single Vehicle Test in Harbin, China

Cited by 23 publications

References 22 publications

Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research

Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research

Speed trajectory planning at signalized intersections using sequential convex optimization

Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning

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