BusTr: Predicting Bus Travel Times from Real-Time Traffic
UC Berkeley（加州大学伯克利分校）；Google Research（谷歌研究院）
Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu
备注：
14 pages, 2 figures, 5 tables. Citation: "Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu (2020). BusTr: Predicting Bus Travel Times from Real-Time Traffic. 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. doi: 10.1145/3394486.3403376"
We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.
Public Transport Movia（莫维亚公共交通公司）；Technical University of Denmark（丹麦技术大学）
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is highly visible in the city landscape of most modern metropolises. More recently, the same information has become critical as input for smart-phone trip planners in order to alert passengers about unreachable connections, alternative route choices and prolonged travel times. More…
Movia Public Transport（Movia公共交通公司）；Technical University of Denmark（丹麦技术大学）
This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression (DQR) and the other on Bayesian Recurrent Neural Networks (BRNN). Both models predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a sampling technique in order to aggregate quantile estimates for link level travel time to yi…
Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms
Xi’an Jiaotong University（西安交通大学）；School of Computer Science and Technology（计算机科学与技术学院）；Guangdong Planning and Designing Institute of Telecommunications Co., Ltd（广东省电信规划设计院有限公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；College of Transportation Engineering, Tongji University（同济大学交通运输工程学院）；SILC Business School, Shanghai University（上海大学悉尼工商学院）；College of Engineering, Zhejiang Normal University（浙江师范大学工学院）
Many factors can affect the predictability of public bus services such as traffic, weather and local events. Other aspects, such as day of week or hour of day, may influence bus travel times as well, either directly or in conjunction with other variables. However, the exact nature of such relationships between travel times and predictor variables is, in most situations, not known. In this paper we develop a framework that allows for flexible modeling of bus travel times through the use of Additive Models. In particular, we model travel times as a sum of linear as well as nonlinear terms that a…
Université de Montréal（蒙特利尔大学）；Polytechnique Montréal（蒙特利尔综合理工学院）
An important aspect of the quality of a public transport service is its reliability, which is defined as the invariability of the service attributes. Preventive measures taken during planning can reduce risks of unreliability throughout operations. In order to tackle reliability during the service planning phase, a key piece of information is the long-term prediction of the density of the travel time, which conveys the uncertainty of travel times. We introduce a reliable approach to one of the problems of service planning in public transport, namely the Multiple Depot Vehicle Scheduling Proble…
Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. …
Passenger Name Records (PNRs) are at the heart of the travel industry. Created when an itinerary is booked, they contain travel and passenger information. It is usual for airlines and other actors in the industry to inter-exchange and access each other's PNR, creating the challenge of using them without infringing data ownership laws. To address this difficulty, we propose a method to generate realistic synthetic PNRs using Generative Adversarial Networks (GANs). Unlike other GAN applications, PNRs consist of categorical and numerical features with missing/NaN values, which makes the use of GA…
Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel real-time GPS bus transit data for over 500 routes of buses operating in New Delhi. The data can be used for modeling various timetable optimization tasks as well as in other domains such as traffic management, travel time estimation, etc. The paper also presents an approach to reduce the waiting time of Delhi buses by analyzing the traffic behavior and proposing a timetable. This algorithm serves as a benchmark for the data…
Institute of Engineering & Management（工程与管理学院）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；BCET Durgapur（杜尔加布尔BCET工程学院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）
Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transports like public buses, allowing her to pre-plan the travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations that a public bus stops. Although straightforward factors stay duration, extracted from unimodal sources like GPS, at these locations look err…
Advancements in Intelligent Transportation System (IES) improve passenger traveling by providing information systems for bus arrival time and counting the number of passengers and buses in cities. Passengers still face bus waiting and seat unavailability issues which have adverse effects on traffic management and controlling authority. We propose a Face Detection based Framework (FDF) to determine passenger seat availability in a camera-equipped bus through face detection which is based on background subtraction to count empty, filled, and total seats. FDF has an integrated smartphone Passenge…
Dalian University of Technology（大连理工大学）；Zhejiang University of Technology（浙江工业大学）；Federation University Australia（澳大利亚联邦大学）；Deakin University（迪肯大学）
Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To reduce this research gap and learn human mobility k…
Carnegie Mellon University, Africa（卡内基梅隆大学（非洲校区））；African Center of Excellence in Data Science, University of Rwanda（卢旺达大学非洲数据科学卓越中心）；Oxford Man Institute of Quantitative Finance, Oxford University（牛津大学牛津曼量化金融研究所）
Modelling, simulation, and forecasting offer a means of facilitating better planning and decision-making. These quantitative approaches can add value beyond traditional methods that do not rely on data and are particularly relevant for public transportation. Lagos is experiencing rapid urbanization and currently has a population of just under 15 million. Both long waiting times and uncertain travel times has driven many people to acquire their own vehicle or use alternative modes of transport. This has significantly increased the number of vehicles on the roads leading to even more traffic and…
Siddaganga Institute of Technology（悉达甘加理工学院）；Gubbi labs, LLP（古比实验室有限责任合伙公司）
The notion of smart cities is being adapted globally to provide a better quality of living. A smart city's smart mobility component focuses on providing smooth and safe commuting for its residents and promotes eco-friendly and sustainable alternatives such as public transit (bus). Among several smart applications, a system that provides up-to-the-minute information like bus arrival, travel duration, schedule, etc., improves the reliability of public transit services. Still, this application needs live information on traffic flow, accidents, events, and the location of the buses. Most cities la…
Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
School of Engineering, University of Guelph（圭尔夫大学工程学院）；EthicalAI（伦理AI）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Tokyo Institute of Technology（东京工业大学）；IBM（国际商业机器公司）；Massachusetts Institute of Technology（麻省理工学院）；Tokyo Medical and Dental University（东京医科齿科大学）
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the volume of international flights among these countries, resulting in extensive social and economic costs. To better understand the situation in a quantitative manner, we used the Opensky network data to clarify flight patterns and flight densities around the world and observe relationships between flight numbers with new infections, and with the economy (unemployment rate) in Barcelona. We found that the number of da…
Deakin University（迪肯大学）；Commonwealth Scientific and Industrial Research Organization（澳大利亚联邦科学与工业研究组织）；Xidian University（西安电子科技大学）；Qilu University of Technology（齐鲁工业大学）；Shandong Computer Science Center（山东省计算中心）
Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a …
