Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Cooperative Adaptive Driving for Platooning Autonomous Self Driving Based on Edge Computing

2019 ◽  
Vol 29 (2) ◽  
pp. 213-225 ◽  
Author(s):  
Ben-Jye Chang ◽  
Ren-Hung Hwang ◽  
Yueh-Lin Tsai ◽  
Bo-Han Yu ◽  
Ying-Hsin Liang
Keyword(s):  
Shock Waves ◽  
Traffic Flow ◽  
Road Traffic ◽  
Edge Computing ◽  
Traffic Information ◽  
Velocity Variation ◽  
Traffic Density ◽  
Cruise Control ◽  
Safe Driving ◽  
Butterfly Effect

Abstract Cooperative adaptive cruise control (CACC) for human and autonomous self-driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is encountered while exhibiting asynchronous brakes that easily lead to backward shock-waves and are difficult to remove. Several critical issues should be addressed in CACC, including (i) difficulties with adaptive steering of the inter-vehicle distances among neighbor vehicles and the vehicle speed, (ii) the butterfly effect, (iii) unstable vehicle traffic flow, etc. To address the above issues in CACC, this paper proposes the mobile edge computing-based vehicular cloud of the cooperative adaptive driving (CAD) approach to avoid shock-waves efficiently in platoon driving. Numerical results demonstrate that the CAD approach outperforms the compared techniques in the number of shock-waves, average vehicle velocity, average travel time and time to collision (TTC). Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.

scholarly journals Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges

2020 ◽  
Vol 12 (4) ◽  
pp. 1501
Author(s):  
Sébastien Dujardin ◽  
Damien Jacques ◽  
Jessica Steele ◽  
Catherine Linard
Keyword(s):  
Climate Change ◽  
Urban Planning ◽  
Mobile Phone ◽  
Urban Areas ◽  
Urban Governance ◽  
Urban Climate ◽  
Spatial Modelling ◽  
Mobile Phone Data ◽  
Transformative Change ◽  
Digital Information

Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.

scholarly journals Urban Wasteful Transport and Its Estimation Methods

2018 ◽  
Vol 10 (12) ◽  
pp. 4562 ◽  
Author(s):  
Xiangyang Cao ◽  
Bingzhong Zhou ◽  
Qiang Tang ◽  
Jiaqi Li ◽  
Donghui Shi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Transport Theory ◽  
Road Traffic ◽  
Urban Traffic ◽  
Flow Density ◽  
Urban Road ◽  
Road Capacity ◽  
Density Relationship

The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

Urban traffic commuting analysis based on mobile phone data

2014 ◽  
Author(s):  
Honghui Dong ◽  
Xiaoqing Ding ◽  
Mingchao Wu ◽  
Yan Shi ◽  
Limin Jia ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Traffic ◽  
Mobile Phone Data

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

Study on Road Traffic Condition Identification Based on Floating Taxi

2011 ◽  
Vol 105-107 ◽  
pp. 2250-2254
Author(s):  
Xin Sheng Yao ◽  
Jian Hua Qu ◽  
Ji Lai Ying
Keyword(s):  
Traffic Management ◽  
Road Traffic ◽  
Sampling Interval ◽  
Urban Traffic ◽  
Traffic Information ◽  
Prototype System ◽  
Data Sampling ◽  
Traffic Condition ◽  
Wireless Communication Protocol ◽  
Information Update

This paper describes a prototype system based on floating taxi for traffic condition identification. The system consists of in-vehicle hardware units placed in floating taxi and backstage database that process all data send from the report units. The communication between the taxi and the database center is based on a very compact wireless communication protocol. The taxi sample size is decided by the variables: section traffic information update cycle, data sampling interval, section covering ratio. The test in a road section showed that the system is operational which could offer useful reference for urban traffic management and resident trips decision.

Effects of Lanes Occupied on Urban Road Traffic Capacity

2014 ◽  
Vol 926-930 ◽  
pp. 3798-3801
Author(s):  
Zhi Wei Yang
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Flow Speed ◽  
Urban Traffic ◽  
Correction Coefficient ◽  
Dynamic Changes ◽  
Traffic Jam ◽  
The Road ◽  
Discharge Capacitance ◽  
Traffic Capacity

The article is research on the influence of urban lane occupied for the road traffic capacity. Under the condition that the density of urban traffic flow is big, and it‘s successional, we consider the quantity of vehicle is continuous. Through analyzing the dynamic changes of the road traffic capacity and its influencing factors after accidents, we can get reasonable suggestions of reducing the length of traffic jam. First we establish a flow-speed-density model to describe the dynamic changes of the road traffic capacity. Then we can compare the traffic flow to the electric current according to its continuity. So the upstream traffic flow and the traffic capacity of the accident cross section are equal to the charging current and the discharging current. And the vehicle queue is translated to the voltage of the charge-discharge capacitance. We can get the length of the vehicle queue by the formula of the capacitance voltage approximately. Finally the correction coefficient is introduced. In conclusion, the road traffic capacity is depended on the distance from the upstream intersection and the lane that the accident happened on and so on. Meanwhile, if we don’t solve the accident timely, the length will rise sharply. It will cause serious traffic jam. So we suggest relevant departments timely deal with the accident, evacuate the traffic, and prompt drivers to change lanes in advance.

Real-Time Monitoring Unit of Traffic Flow

2012 ◽  
Vol 588-589 ◽  
pp. 1058-1061
Author(s):  
Ting Zhang ◽  
Zhan Wei Song
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Traffic Information ◽  
Real Time Monitoring ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Transit Use ◽  
Control Traffic

With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

scholarly journals Modeling Tourists’ Departure Time considering the Influence of Multisource Traffic Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shijun Yu ◽  
Siyuan Zhang ◽  
Shejun Deng ◽  
Tao Ji ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Multinomial Logit Model ◽  
Economic Benefits ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Urban Residents ◽  
Related Factors ◽  
Departure Time ◽  
Intelligent Technology

The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.

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