Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services
| Autor: | Ryan Johnston, P Franco, Ecaterina McCormick |
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| Rok vydání: | 2020 |
| Předmět: |
Agent-based model
Service (business) Mode of transport 050210 logistics & transportation Transportation planning business.industry Computer science 05 social sciences 0211 other engineering and technologies Transportation 02 engineering and technology Management Science and Operations Research Business model Transport engineering Mobile phone Public transport 0502 economics and business 021108 energy Last mile business Civil and Structural Engineering |
| Zdroj: | Transportation Research Part A: Policy and Practice. 131:244-266 |
| ISSN: | 0965-8564 |
| DOI: | 10.1016/j.tra.2019.09.038 |
| Popis: | Demand Responsive Transport (DRT), covering the first/last mile of a journey, plays a pivotal role in the delivery of a seamless integrated door-to-door service, which is a fundamental requirement for the implementation of Mobility as a Service (MaaS). Business models currently in use do not deliver sustainable and durable DRT in urban areas. This can be minimised using transport modelling tools ahead of the operation phase. However, transport models are not fit for purpose when it comes to model on-demand shared mobility services and the integration of these services in a complex public transport ecosystem. This paper focuses on how to model demand for ride-shared mobility services and how to plan for these services when running in integration with mass transit. An Agent Based Model (ABM), built in the open-source Multi-Agent Transport Simulation (MatSim) platform for Bristol (UK), has used an activity-based approach to model demand for two New Mobility Services (NMS). This was then generated using anonymised and aggregated Mobile phone Network Dataset (MND), both as a trip-based and trip chains dataset to assess the capabilities of MND. Results show that the simulations built using the trip chains MND datasets (722,752 agents generated) lead to better insights in users’ travel patterns. An advanced method using additional data sources covering land-use (location of business, services and transport facilities) was used to infer purpose and mode of transport during the multimodal journeys. The output of the ABM predicts demand for two flexible on-demand services, identifying best routes to maximise the number of users served and quantifying the benefits in the integration with public transport services and in modal shift from private cars. This is expected to be useful either for Local Authorities for transport planning purposes, and for operators looking at financially sustainable DRT. |
| Databáze: | OpenAIRE |
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