Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

The Characteristics of Resident Travel in Beijing Xizhimen Transport Terminal

2011 ◽  
Vol 225-226 ◽  
pp. 1212-1217
Author(s):  
Xue Mei Li ◽  
Jing Yin ◽  
Qian Che
Keyword(s):  
Travel Behavior ◽  
Transportation Planning ◽  
Large Scale ◽  
Transportation Network ◽  
Urban Transportation ◽  
The Other ◽  
Theoretical Support ◽  
Controllable Factors ◽  
Disaggregate Model ◽  
Core Facilities

Transport Terminals are core facilities of urban transportation system, and the joint of different transportation in urban transportation network. Because of their functions and place, they are faced with huge traffic pressure. So the research about the characteristics of resident travel in transport terminals could provide theoretical support for urban transportation planning, organizing and managing, finally improve urban transportation satisfaction among residents. Against this background, Xizhimen as the research object is a representative transport terminal in Beijing. Based on large-scale investigation, on one hand, the characteristics of residents travel behavior are analyzed qualitatively; on the other hand, by building the Disaggregate Model, analyze the utility functions of different travel modes quantitatively, to find some controllable factors to optimize transport terminal and improve their satisfaction.

Study on Park and Ride Locating Based on Transportation Demand Management

2014 ◽  
Vol 1030-1032 ◽  
pp. 2254-2259
Author(s):  
Jin De Cai ◽  
Ke Zhang
Keyword(s):  
Traffic Congestion ◽  
Transportation Network ◽  
Demand Management ◽  
Urban Transportation ◽  
Urban Traffic ◽  
Transportation Costs ◽  
Construction Costs ◽  
Transportation Management ◽  
Transportation Demand ◽  
Park And Ride

With the increasingly serious problem of urban traffic congestion, more attention is focused on the Park and Ride (P&R) schemes based on urban transportation demand (TDM) management. The P&R locating research, as an important part of the scheme, plays an important role to strengthen the transportation management. On the basis of identifying all the potential P&R locations, and from the macroscopic perspective of urban transportation network, this paper establishes a model of P&R locating in order to minimize their construction costs as well as the total transportation costs. Example analysis is finally carried out with the help of Lingo software, thus testifying the validity of this research.

scholarly journals Research on Congestion Pricing in Multimode Traffic considering Delay and Emission

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hongna Dai ◽  
Enjian Yao ◽  
Rui Zhao
Keyword(s):  
Bilevel Programming ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Rapid Development ◽  
Congestion Pricing ◽  
Urban Traffic ◽  
Pricing Model ◽  
Network Efficiency ◽  
Traffic Demand ◽  
Model Based

Rapid development of urbanization and automation has resulted in serious urban traffic congestion and air pollution problems in many Chinese cities recently. As a traffic demand management strategy, congestion pricing is acknowledged to be effective in alleviating the traffic congestion and improving the efficiency of traffic system. This paper proposes an urban traffic congestion pricing model based on the consideration of transportation network efficiency and environment effects. First, the congestion pricing problem under multimode (i.e., car mode and bus mode) urban traffic network condition is investigated. Second, a traffic congestion pricing model based on bilevel programming is formulated for a dual-mode urban transportation network, in which the delay and emission of vehicles are considered. Third, an improved mathematical algorithm combining successive average method with the genetic algorithm is proposed to solve the bilevel programming problem. Finally, a numerical experiment based on a hypothetical network is performed to validate the proposed congestion pricing model and algorithm.

scholarly journals Military Simulation Big Data: Background, State of the Art, and Challenges

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xiao Song ◽  
Yulin Wu ◽  
Yaofei Ma ◽  
Yong Cui ◽  
Guanghong Gong
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Rapid Development ◽  
Military Training ◽  
Great Success ◽  
Service Oriented Architectures ◽  
Weapon Systems ◽  
Service Oriented ◽  
High Resolution Models ◽  
Future Work

Big data technology has undergone rapid development and attained great success in the business field. Military simulation (MS) is another application domain producing massive datasets created by high-resolution models and large-scale simulations. It is used to study complicated problems such as weapon systems acquisition, combat analysis, and military training. This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then we looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Two frameworks were also surveyed to trace the development of the underlying software platform. Finally, we identified some key challenges and proposed a framework as a basis for future work. This framework considered both the simulation and big data management at the same time based on layered and service oriented architectures. The objective of this review is to help interested researchers learn the key points of MS big data and provide references for tackling the big data problem and performing further research.

scholarly journals Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Lemonde ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Big Data ◽  
City Council ◽  
Urban Traffic ◽  
Integrative Analysis ◽  
Context Aware ◽  
Traffic Data ◽  
Situational Context ◽  
Transport Modes ◽  
The City ◽  
Urban Data

AbstractWorldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

scholarly journals Dynamical efficiency for multimodal time-varying transportation networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leonardo Bellocchi ◽  
Vito Latora ◽  
Nikolas Geroliminis
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Transportation Networks ◽  
Transportation System ◽  
Urban Traffic ◽  
Flow Conditions ◽  
Traffic Condition ◽  
Spatial Systems ◽  
Layered Networks ◽  
Critical Zones

AbstractSpatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.

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