scholarly journals Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development

2020 ◽  
Vol 34 (04) ◽  
pp. 4020-4027
Author(s):  
Yongshun Gong ◽  
Zhibin Li ◽  
Jian Zhang ◽  
Wei Liu ◽  
Jinfeng Yi
Keyword(s):  
Real World ◽  
Intelligent Transportation Systems ◽  
Domain Knowledge ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Metro Station ◽  
Practical Applications ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Transportation Development

Recently, practical applications for passenger flow prediction have brought many benefits to urban transportation development. With the development of urbanization, a real-world demand from transportation managers is to construct a new metro station in one city area that never planned before. Authorities are interested in the picture of the future volume of commuters before constructing a new station, and estimate how would it affect other areas. In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems. For example, an accurate PPF predictor can provide invaluable knowledge to designers, such as the advice of station scales and influences on other areas, etc. To address this problem, we propose a multi-view localized correlation learning method. The core idea of our strategy is to learn the passenger flow correlations between the target areas and their localized areas with adaptive-weight. To improve the prediction accuracy, other domain knowledge is involved via a multi-view learning process. We conduct intensive experiments to evaluate the effectiveness of our method with real-world official transportation datasets. The results demonstrate that our method can achieve excellent performance compared with other available baselines. Besides, our method can provide an effective solution to the cold-start problem in the recommender system as well, which proved by its outperformed experimental results.

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

scholarly journals Sustainable urban transportation development in China: A behavioral perspective

2021 ◽  
Author(s):  
Shuai Ling ◽  
Shoufeng Ma ◽  
Ning Jia
Keyword(s):  
Traffic Management ◽  
Rapid Development ◽  
Demand Management ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Special Cases ◽  
Traffic Systems ◽  
Four Levels ◽  
Transportation Development

AbstractThe rapid development of economics requires highly efficient and environment-friendly urban transportation systems. Such requirement presents challenges in sustainable urban transportation. The analysis and understanding of transportation-related behaviors provide one approach to dealing with complicated transportation activities. In this study, the management of traffic systems is divided into four levels with a structural and systematic perspective. Then, several special cases from the perspective of behavior, including purchasing behaviors toward new energy vehicles, choice behaviors toward green travel, and behavioral reactions toward transportation demand management policies, are investigated. Several management suggestions are proposed for transportation authorities to improve sustainable traffic management.

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Jelena Fiosina ◽  
Maxims Fiosins, Jörg P. Müller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Social Impact ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimization Techniques ◽  
Next Generation ◽  
Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

scholarly journals Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

2019 ◽  
Vol 11 (18) ◽  
pp. 4989 ◽  
Author(s):  
Wei Yu ◽  
Hua Bai ◽  
Jun Chen ◽  
Xingchen Yan
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Rapid Development ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Space Time ◽  
Smart Cards ◽  
The Sustainable Development ◽  
Passenger Flow ◽  
Challenges And Opportunities

The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

2016 ◽  
Vol 20 (7) ◽  
pp. 1181-1191 ◽  
Author(s):  
Chengdong Li ◽  
◽  
Yisheng Lv ◽  
Jianqiang Yi ◽  
Guiqing Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Fuzzy Rule ◽  
Transportation Systems ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Traffic Flow Prediction ◽  
Fast Learning ◽  
Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

A Low-Cost Real-World Planning Strategy for Deploying a Dedicated Short-Range Communications Roadside Unit on a Highway Off-Ramp

2018 ◽  
Vol 2672 (19) ◽  
pp. 124-134 ◽  
Author(s):  
Jonathan B. Walker ◽  
Kevin Heaslip
Keyword(s):  
Real World ◽  
Intelligent Transportation Systems ◽  
Short Range ◽  
Environmental Changes ◽  
Low Cost ◽  
Transportation Systems ◽  
Planning Strategy ◽  
Dedicated Short Range Communications ◽  
Roadside Unit ◽  
World Environment

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

scholarly journals Big Data analysis: Intelligent Transportation Development Engine

2014 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Qin Xiao
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Information Age ◽  
Large Data ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Advantages And Disadvantages ◽  
Transportation Development

<p>With the development of the times, people have unwittingly entered the information age. The information age has become a large amount of data bursting characteristics of the new era. In this feature people still seek to improve the production and quality of life. For the development of intelligent transportation needs of people's lives and make the real world, but in the construction of intelligent transportation among a large number of information data also adds to its change and difficulty, how to build an intelligent era of big data, security, low-cost, efficient and convenient of intelligent transportation systems become today people study. From the era of big data to intelligent traffic changes brought advantages and disadvantages, the era of big data to bring intelligent traffic problems and challenges, as well as the integration platform for massive data intelligent transportation intelligent transportation demand and large data structures has done a simple elaborate, it can provide some suggestions for areas of research that scientists.</p>

scholarly journals An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity

2020 ◽  
Vol 54 (2) ◽  
pp. 59-73
Author(s):  
Yang Wang ◽  
Yu Xiao ◽  
Jianhui Lai ◽  
Yanyan Chen
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Domain Knowledge ◽  
Rapid Development ◽  
Transportation Systems ◽  
Nearest Neighbour ◽  
Flow Data ◽  
Fundamental Parameters ◽  
Determination Of Parameters

Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.

Urban Transportation Planning Education Revisited: Reading the Dials and Steering the Ship

2003 ◽  
Vol 1848 (1) ◽  
pp. 57-63 ◽  
Author(s):  
C. Jotin Khisty ◽  
Shinya Kikuchi
Keyword(s):  
Real World ◽  
Transportation Planning ◽  
Public Involvement ◽  
Urban Transportation ◽  
Applied Ethics ◽  
Course Evaluation ◽  
Transportation Systems ◽  
Systems Methodology ◽  
Soft Systems ◽  
The Real

Now, more than ever, the planning and management of transportation systems in the real world is beset by change and uncertainty. Under these conditions, the quality of transportation education continues to be a matter of concern for the profession because of continuing demands and commitments at several levels. From an academic standpoint, topics such as public involvement, strategic management of capital resources, soft systems methodology, and applied ethics need to be incorporated in the curriculum in more substantial ways. On the basis of the demands of professional practice, as suggested by hiring agencies, the political working of the real world also needs to be understood by entry-level graduates. These and other relevant issues, relevant particularly for a graduate-level course on urban transportation planning, are described and discussed. On the basis of the “Millennium State-of-the-Art and Future Directions White Paper” sponsored by the Committee on Transportation Education, students are expected to have a deeper knowledge and acquire greater skills in the following four topics: intermodalism and systemicity, soft systems methodologies, applied ethics, and communication. A sample course outline incorporating these suggestions based on a three-credit course offered at the Illinois Institute of Technology, Chicago, in 2001 is described. Finally, the composition of the class and the student course evaluation are discussed. Overall, the course was well received by students.

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