Clairvoyance: Intelligent Route Planning for Electric Buses Based on Urban Big Data

• URL: http://arxiv.org/abs/2112.04682v1
• Date: Thu, 9 Dec 2021 03:35:22 GMT
• Title: Clairvoyance: Intelligent Route Planning for Electric Buses Based on Urban Big Data
• Authors: Xiangyong Lu, Kaoru Ota, Mianxiong Dong, Chen Yu, and Hai Jin
• Abstract summary: We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people's trips. We evaluate our approach through extensive experiments on real-world data sources in Zhuhai, China.
• Score: 23.316569763678455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce local carbon emissions. In order to cut carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people's trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people's trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city.

Related papers

• Fair Railway Network Design [10.044230726002153]
  In a country, one may want to minimise the sum of travel duration of all inhabitants. This corresponds to a purely utilitarian view and does not involve any fairness consideration. On the other hand, a more egalitarian view will allow some people to travel between peripheral cities without having to go through a central city.
  arXiv  Detail & Related papers  (2024-09-03T12:13:05Z)
• Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data [45.13152172664334]
  We design traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles. We leverage real-world trajectory data from the I-24 highway in Tennessee. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves.
  arXiv  Detail & Related papers  (2024-01-18T00:50:41Z)
• Valuation of Public Bus Electrification with Open Data [1.2740654484866891]
  This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. We develop physics-informed machine learning models to evaluate the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route.
  arXiv  Detail & Related papers  (2022-09-25T00:02:23Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• End-to-end Interpretable Neural Motion Planner [78.69295676456085]
  We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios. We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
  arXiv  Detail & Related papers  (2021-01-17T14:16:12Z)
• Design and Evaluation of Electric Bus Systems for Metropolitan Cities [0.0]
  A shift from conventional diesel buses to electric buses comes with several benefits in terms of reduction in local pollution, noise, and fuel consumption. This paper proposes the relevant vehicle technologies, powertrain, and charging systems, which, in combination, provides a comprehensive methodology to design an Electric Bus.
  arXiv  Detail & Related papers  (2020-10-26T12:49:35Z)
• Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine [43.09401975244128]
  We present Polestar, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs. We then introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. Experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectiveness.
  arXiv  Detail & Related papers  (2020-07-11T05:14:52Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)
• Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets [7.2775693810940565]
  Public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs) Because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. We present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets.
  arXiv  Detail & Related papers  (2020-04-10T16:31:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.