Analyzing Demand with Respect to Offer of Mobility
Source abstract
Abstract
A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation Planning and Optimization · Human Mobility and Location-Based Analysis · Transportation and Mobility Innovations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Distributed Systems and Internet Tech Lab, DISIT Lab, Department of Information Engineering, University of Florence, DINFO, 50139 Firenze, Italy
Funding
Tuscany Region MOSAIC project
References (46)
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1. Afrin, T., and Yodo, N. (2020). A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability, 12.10.3390/su12114660 ↗
2. Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions10.1016/j.future.2020.08.017 ↗
3. Bellini, P., Bilotta, S., Nesi, P., Paolucci, M., and Soderi, M. (2018, January 12–15). Real-Time Traffic Estimation of Unmonitored Roads. Proceedings of the 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Athens, Greece.10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-6 ↗
4. Validation of road vehicle and traffic emission models—A review and meta-analysis10.1016/j.atmosenv.2010.05.022 ↗
5. Bilotta, S., and Nesi, P. (2022). Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction. Sensors, 22.10.3390/s22093382 ↗
6. A Review of Service Reliability Measures for Public Transportation Systems
7. Assessing travel time reliability in transport appraisal10.1016/j.jtrangeo.2009.06.012 ↗
8. Preferences of public transit commuters: Evidence from smart card data in Singapore10.1016/j.jue.2020.103288 ↗
9. (2020, October 16). Static GTFS Manager. Available online: https://static-gtfs-manager.herokuapp.com/.
10. Antunes, H., Figueiras, P., Costa, R., Teixeira, J., and Jardim, R. (2019, January 27). Analysing Public Transport data through the use of Big Data technologies for urban mobility. Proceedings of the 2019 International Young Engineers Forum (YEF-ECE), Hong Kong.10.1109/YEF-ECE.2019.8740816 ↗
11. Widyawan, B., Putra, D.W., Kusumawardani, S.S., Widhiyanto, B.T.Y., and Habibie, F. (2017, January 27–28). Big data analytic for estimation of origin-destination matrix in Bus Rapid Transit system. Proceedings of the 2017 3rd International Conference on Science and Technology—Computer (ICST), Dubai, United Arab Emirates.10.1109/ICSTC.2017.8011872 ↗
12. Tanaka, M.T., Kimata, T., and Arai, T. (2016, January 10–14). Estimation of Passenger Origin-Destination Matrices and Efficiency Evaluation of Public Transportation. Proceedings of the 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Kumamoto, Japan.10.1109/IIAI-AAI.2016.11 ↗
13. Zapata, L.P., Flores, M., Larios, V., Maciel, R., and Antunez, E.A. (2019, January 14–17). Estimation of people flow in public transportation network through the origin-destination problem for the South-Eastern corridor of Quito city in the smart cities context. Proceedings of the 2019 IEEE International Smart Cities Conference (ISC2), Casablanca, Morocco.10.1109/ISC246665.2019.9071778 ↗
14. Travel satisfaction effects of changes in public transport usage10.1007/s11116-015-9576-3 ↗
15. Short term ridership prediction in public transport by processing smart card data10.3141/2535-12 ↗
16. Combinatorial Auction-Based Pricing for Multi-Tenant Autonomous Vehicle Public Transportation System10.1109/TITS.2015.2490800 ↗
17. Shang, S., Guo, D., Liu, J., and Liu, K. (2014, January 15–18). Human Mobility Prediction and Unobstructed Route Planning in Public Transport Networks. Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management, Brisbane, Australia.10.1109/MDM.2014.66 ↗
18. Models of bus boarding and alighting dynamics10.1016/j.tra.2014.09.007 ↗
19. The Potential of E-ticketing for Public Transport Planning: The Piedmont Region Case Study10.1016/j.trpro.2016.12.001 ↗
20. A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems10.1109/TITS.2017.2686877 ↗
21. Real-time schedule adjustments for autonomous public transport vehicles10.1016/j.trc.2019.10.004 ↗
22. Oviedo, D., Granada, I., and Perez-Jaramillo, D. (2020). Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá. Sustainability, 12.10.3390/su12051732 ↗
23. Optimizing urban rail timetable under time-dependent demand and oversaturated conditions10.1016/j.trc.2013.08.016 ↗
24. Analysis of a new public-transport-service concept: Customized bus in China10.1016/j.tranpol.2015.02.004 ↗
25. Energy-Efficient Subway Train Scheduling Design with Time-Dependent Demand Based on an Approximate Dynamic Programming Approach10.1109/TSMC.2018.2818263 ↗
26. Analyzing Multimodal Public Transport Journeys in London with Smart Card Fare Payment Data10.3141/2121-06 ↗
27. Embedded system to evaluate the passenger comfort in public transportation based on dynamical vehicle behavior with user’s feedback10.1016/j.measurement.2013.08.068 ↗
28. Wu, W., Zheng, Y., Cao, N., Zeng, H., Ni, B., Qu, H., and Ni, L.M. (2017, January 18–21). MobiSeg: Interactive region segmentation using heterogeneous mobility data. Proceedings of the 2017 IEEE Pacific Visualization Symposium (PacificVis), Seoul, Korea.10.1109/PACIFICVIS.2017.8031583 ↗
29. Performance contributors of bus rapid transit systems: An ordered choice approach10.1016/j.eap.2020.07.004 ↗
30. Horni, A., Nagel, K., and Axhausen, K.W. (2016). The Multi-Agent Transport Simulation MATSim, Ubiquity Press London.
31. Recent Development and Applications of SUMO—Simulation of Urban Mobility
32. Smith, L., Beckman, R., and Baggerly, K. (1995). TRANSIMS: Transportation Analysis and Simulation System, Los Alamos National Laboratory.10.2172/88648 ↗
33. Allaboard: Visual exploration of cellphone mobility data to optimise public transport
34. (2022, September 05). MOSAiC Project: Mobility 4.0 for Smart Cities. Available online: https://www.disit.org/drupal/?q=node/7174.
35. Km4City ontology building vs. data harvesting and cleaning for smart-city services10.1016/j.jvlc.2014.10.023 ↗
36. Smart City IoT Platform Respecting GDPR Privacy and Security Aspects10.1109/ACCESS.2020.2968741 ↗
37. Bellini, E., Bellini, P., Cenni, D., Nesi, P., Pantaleo, G., Paoli, I., and Paolucci, M. (2021). An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors, 21.10.3390/s21020435 ↗
38. Wi-Fi based city users’ behaviour analysis for smart city10.1016/j.jvlc.2017.08.005 ↗
39. Dynamic Origin-Destination Flow Estimation Using Cellular Communication System10.1109/TVT.2007.912336 ↗
40. (2019, April 01). OpenStreetMap. Available online: www.openstreetmap.org.
41. (2022, September 05). Istituto Nazionale di Statistica. Available online: https://www.istat.it/en/.
42. (2022, September 05). The Region Tuscany Digital Portal. Available online: http://www.regione.toscana.it/.
43. (2022, September 05). ServiceMap. Available online: http://servicemap.disit.org/.
44. Nesi, P., Badii, C., Bellini, P., Cenni, D., Martelli, G., and Paolucci, M. (2016, January 18–20). Km4City Smart City API: An Integrated Support for Mobility Services. Proceedings of the 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, USA.10.1109/SMARTCOMP.2016.7501702 ↗
45. (2022, September 05). Overpass Turbo Tool. Available online: http://overpass-turbo.eu/.
46. Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand10.1016/j.tra.2013.06.005 ↗