scholarly journals Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 195
Author(s):  
Hua Chen ◽  
Ming Cai ◽  
Chen Xiong
Keyword(s):  
Travel Demand ◽  
Population Distribution ◽  
Human Mobility ◽  
Urban Transport ◽  
License Plate ◽  
Traffic Patterns ◽  
Trajectory Data ◽  
Transport Planning ◽  
Population Distributions ◽  
Single Dataset

With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determine whether a single-dataset-based result can meet the requirement of urban transport planning. In response to this problem, we attempted to obtain traffic patterns and population distributions from the perspective of multisource traffic data using license plate recognition (LPR) data and cellular signaling (CS) data. Based on the two kinds of datasets, identification methods of residents’ travel stay point are proposed. For LPR data, it was identified based on different vehicle speed thresholds at different times. For CS data, a spatiotemporal clustering algorithm based on time allocation was proposed to recognize it. We then used the correlation coefficient r and the significance test p-values to analyze the correlations between the CS and LPR data in terms of the population distribution and traffic patterns. We studied two real-world datasets from five working days of human mobility data and found that they were significantly correlated for the stay and move population distributions. Then, the analysis scale was refined to hour level. We also found that they still maintain a significant correlation. Finally, the origin–destination (OD) matrices between traffic analysis zones (TAZs) were obtained. Except for a few TAZs with poor correlations due to the fewer LPR records, the correlations of the other TAZs remained high. It showed that the population distribution and traffic patterns computed by the two datasets were fairly similar. Our research provides a method to improve the analysis of complex travel patterns and behaviors and provides opportunities for travel demand modeling and urban transport planning. The findings can also help decision-makers understand urban human mobility and can serve as a guide for urban management and transport planning.

Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management

2018 ◽  
Vol 13 (2) ◽  
pp. 380-386 ◽  
Author(s):  
Ko Ko Lwin ◽  
◽  
Yoshihide Sekimoto ◽  
Wataru Takeuchi
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Disaster Management ◽  
Research Collaboration ◽  
Query Language ◽  
Population Distribution ◽  
Human Mobility ◽  
Urban Transport ◽  
Geographical Information ◽  
Transport Planning

This article reports the development of a geographical information system (GIS) embedded text-based geospatial Big Data research toolbox (BigGIS-RTX) designed especially for mobile CDR (Call Details Record) data processing in urban transport planning and disaster management. BigGIS-RTX is a standalone computer program that aims to provide a bridge between geospatial Big Data and end users (i.e. students and researchers) by reducing difficulties in handling geospatial Big Data processing and analysis tasks. This research toolbox makes it possible to handle text-based geospatial Big Data cleaning, formatting, subsetting, and extraction by keywords or structured query language (SQL), CDR data aggregation by base transceiver stations (BTSs), generation of origin–destination (OD) trips, OD matrices, and OD routes, and computation of OD links. Moreover, this research toolbox can be integrated with current commercial GIS software to perform further geospatial analysis functions to improve spatial decision making in urban and transport planning and disaster management. In this report, we discuss two current research outputs using BigGIS-RTX: first, multitemporal grid square population estimation and second, human mobility studies in transportation planning. These research outputs are essential for disaster management and emergency preparedness in terms of providing knowledge and information about population distribution changes over space and time, human mobility flow by a user defined time frame, and travel volume or link count information for individual road segments. This research is part of the core project “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar” in a research collaboration between Yangon Technological University, Myanmar, and The University of Tokyo, Japan, sponsored by the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA).

scholarly journals Application of Trip Generation Models for Urban Transport Planning In a Data Scarce Developing Country City: The Case of Dar es Salaam

2014 ◽  
Vol 35 (2) ◽  
pp. 99-112
Author(s):  
Hanniabl Bwire
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Developed Countries ◽  
Dar Es Salaam ◽  
Urban Transport ◽  
Interactive Effects ◽  
Trip Generation ◽  
Transport Planning ◽  
Travel Demand Forecasting ◽  
Forecasting Models

With the increase in travel demand and traffic management problems in many developingcountries cities, travel demand forecasting models are being employed increasingly tomake informed decisions about the operational improvements to the existing transportationsystem and the design and performance of future transportation systems. The mainadvantage of using travel demand forecasting models for such purposes is that they arecapable of capturing the interactive effects of different components of the system understudy. However, for some time now there have been concerns about the application oftransport planning models in developing countries. The concerns have been mainly inrelation to the variables, coefficients and models borrowed from developed countries. Thispaper first discusses the characteristics of transport problems in developing cities andprovides a review of trip generation modelling approaches. Then, the discussion extendsfurther to cover available data for urban transport planning and trip generation modelsthat have found application in Dar es Salaam, Tanzania. The paper concludes byhighlighting how available data sources and trip generation modelling approach can beimproved to cope with the dynamic conditions in Dar es Salaam.

scholarly journals Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

2021 ◽  
Vol 13 (4) ◽  
pp. 2178
Author(s):  
Songkorn Siangsuebchart ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Surachet Pravinvongvuth
Keyword(s):  
Performance Assessment ◽  
Public Transport ◽  
Human Mobility ◽  
Open Data ◽  
Capital City ◽  
Metropolitan Region ◽  
Traffic Information ◽  
Mobility Patterns ◽  
Transport Planning ◽  
Java Program

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

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