Transit Travel Demand Analysis Method Based on APTS Multi-Source Data

2013 ◽  
Vol 361-363 ◽  
pp. 2122-2126
Author(s):  
Jun Chen ◽  
Xiao Hua Li ◽  
Lan Ma

Traditional transit travel information is acquired by Trip Sample Survey which has some disadvantages including high cost and short data lifecycle. This paper researched transit travel demand analysis method using Advanced Public Transportation Systems (APTS) data. The study collected APTS data of Nanning City in China and established APTS multi-source data analysis platform applying data warehouse technology. Based on key problems research, the paper presented the analysis procedure and content. Then, this study proposed the core algorithms of the method which are determinations of boarding bus stops, alighting bus stops and transfer bus stops of smart card passengers. Finally, these algorithms programs are experimented using large scale practical APTS data. The results show that this analysis method is low cost, operability and high accuracy.

2014 ◽  
Vol 71 (5) ◽  
Author(s):  
Adzlia N.N.A. Rahman ◽  
Zaharah M.Yusoff ◽  
Dasimah Omar ◽  
Intan S. Aziz

Intensifying numbers of travel demand draws problem to sustainable transport system because of poor facilities, services, frequency and punctuality due to lack of decent planning and design. Increasing numbers of the vehicle ownership has developed the road networks insufficient that leads to congestions. The issues of travel characteristics chosen by the people to their employment area that will show the travel pattern is the core point in this research. The research methodology consist of the interview sessions to the authorise personals and from the distribution of questionnaire survey forms to the resident of low-cost housing areas in Melaka Tengah District in Malaysia. Then data collected were processed using Social Package Statistical Software (SPSS) to show analytical result. This process will displayed the respondent’s travel characteristics and determine their preference in transportation mode to the employment area. Analytical result showed that the impacts from more than half of the respondents choose to travel by car as transportation mode to working area will come to consequences of traffic condition which lead to congested road. These findings will further help bringing in improvement to existing routes and public transportation systems so it will be optimally utilised for a better daily commute. 


2012 ◽  
Vol 253-255 ◽  
pp. 1918-1921
Author(s):  
Jun Chen ◽  
Zhao Fei Wang

In order to apply smart card data in decision-making of public transportation planning and management, the paper researched estimating method of alighting bus stops of smart card passengers. Based on Trip-chain thought, the paper presented estimation algorithm applying the three hypotheses of “Next Trip”, “Last Trip” and “Return Trip”. Then, the algorithm was tested and analyzed using large-scale actual data of Advanced Public Transportation Systems of Nanning City in China. The results show that Trip-chain Method can estimate majority of alighting bus stops.


2020 ◽  
Author(s):  
Gaofeng pan ◽  
Mohamed-Slim Alouini

In order to fulfill transportation demands, people have well-explored ground, waterborne, and high-altitude spaces (HAS) for transportation purposes, as well as the underground space under cities (namely, subway systems). However, due to the increased burdens of population and urbanization in recent decades, huge pressures on public transportation and freight traffic are introduced to cities, plaguing the governors and constraining the development of economics. By observing the fact that near-ground space (NGS) has rarely been utilized, researchers and practitioners started to re-examine, propose and develop flying cars, which are not a totally novel idea, aiming at solving the traffic congestion problem and releasing the strains of cities. Flying cars completely differ from traditional grounded transportation systems, where automobiles/trains are suffering track limitations and are also different from the air flights in HAS for long-distance transfer. Therefore, while observing the lack of specific literature on flying cars and flying car transportation systems (FCTS), this paper is motivated to study the advances, techniques, and challenges of FCTS imposed by the inherent nature of NGS transportation and to devise useful proposals for facilitating the construction and commercialization of FCTS, as well as to facilitate the readers understanding of the incoming FCTS. We first introduce the increased requirements for transportation and address the advantages of flying cars. Next, a brief overview of the developing history of flying cars is presented in view of both timeline and technique categories. Then, we discuss and compare the state of the art in the design of flying cars, including take-off \& landing (TOL) modes, pilot modes, operation modes, and power types, which are respectively related to the adaptability, flexibility & comfort, stability & complexity, environmental friendliness of flying cars. Additionally, since large-scale operations of flying cars can improve the aforementioned transportation problem, we also introduce the designs of FCTS, including path and trajectory planning, supporting facilities and commercial designs. Finally, we discuss the challenges which might be faced while developing and commercializing FCTS from three aspects: safety issues, commercial issues, and ethical issues.


Author(s):  
Suresh Sankaranarayanan ◽  
Paul Hamilton

Public transportation in many countries is being used as a means of transport for travelling and accordingly people would prefer these public transportation to be scheduled properly, on time and the frequency be adequately fixed for commuters to make good use of it. It has been found that quite an amount of research work has been carried out, by way of using RFID technology in the public transportation systems towards the tracking of passengers when they board and exit buses. In addition research has also been carried out in using GPS towards the tracking of buses along with RFID technology at traffic lights, bus stops, intersections etc and also displaying expected arrival times on LCD screen at bus stops along with their current positions. Taking these aspects into consideration, an intelligent mobile bus tracking system for the Jamaican Urban Transport Corporation has been proposed and validated as a case study. The proposed system also enables commuters towards tracking the bus of their choice and also knowing their expected arrival times. So taking the above aspects into consideration, in this research the authors have proposed and validated on how control center of a bus company could track the location of a bus based on information received from RFID reader and GPS Transmitter positioned at various Bus stops and in the Bus and accordingly the expected time of arrival calculated for displaying the information on commuter's handset via Gmap. The implementation of the bus tracking scheme has been carried out using Adobe Flash player and Java.


2019 ◽  
Author(s):  
Yair Fogel-Dror ◽  
Shaul R. Shenhav ◽  
Tamir Sheafer

The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.


2021 ◽  
Author(s):  
Abhilasha Dubey ◽  
Sanjay Upadhyay ◽  
Manjeet Mehta

Rapid, reliable and robust method for the detection of SARS-CoV-2 is an indispensable need for diagnostics. The development of diagnostic methods will aid to address further waves of the pandemic potentially with rapid surveillance of disease and to allay the fears. To meet this challenge, we have developed a rapid RT-qPCR method for the detection of 3 target genes or confirmatory genes in less than 30 minutes. The assay showed 100% sensitivity and 100% specificity when tested on 120 samples. We compared a conventional extraction based method with extraction-free method, and then further reduced the run time of extraction free method. Additionally, we have validated our rapid RT-qPCR method for the assessment of pooled samples. We hereby propose a most reliable approach for the mass screening of samples with ease of operation at a low cost. Finally we designed a single tube analysis method which provides qualitative as well as quantitative results in minimum time.


Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098


2021 ◽  
Vol 3 (1) ◽  
pp. 29-59
Author(s):  
Yair Fogel-Dror ◽  
Shaul R. Shenhav ◽  
Tamir Sheafer

Abstract The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.


Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.


2021 ◽  
Author(s):  
Donatella Darsena ◽  
Giacinto Gelli ◽  
Ivan Iudice ◽  
Francesco Verde

Management of crowd information in public transportation (PT) systems is crucial to foster sustainable mobility, by increasing the user’s comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in various segments of the PT system (buses/trams/trains, railway/subway stations, and bus stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) adapt in real-time PT system operations, by modifying service frequency, timetables, routes, and so on; (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to traditional intelligent transportation system (ITS) platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.


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