Abstract
Reducing congestion has been one of the critical targets of transportation policies, particularly in cities in developing countries suffering severe and chronic traffic congestions. Several traditional measures have been in place but seem not very successful. This paper applies the agent-based transportation model MATSim for a transportation analysis in Bangkok to assess the impact of spatiotemporal transportation demand management measures. We collect required data for the simulation from various data sources and apply maximum likelihood estimation with the limited data available. We investigate two demand management scenarios, peak time shift, and decentralization. As a result, we found that these spatiotemporal peak shift measures are effective for road transport to alleviate congestion and reduce travel time. However, the effect of those measures on public transport is not uniform but depends on the users’ circumstances. On average, the simulated results indicate that those measures increase the average travel time and distance. These results suggest that demand management policies require considerations of more detailed conditions to improve usability. The study also confirms that microsimulation can be a tool for transport demand management assessment in developing countries.
Highlights
The global passenger transportation demand was 51 trillion passenger-km in 2014, which is expected to more than double and reach 110 trillion passenger-km in 2060 [1].Most of the increase is expected to emerge in developing countries where the population and economy are growing significantly
(BAU case case and and time-shift time-shift case). We applied these scenarios with MATSim and compared their simulated travel times We applied these scenarios withchosen
The sample rate was chosen to be for road transport and for public transport in the simulation, which is a reasonable approach in agent-based modeling to transport simulation, which is volumes a reasonable approach agent-based modeling to economizeinonthe runtime
Summary
The global passenger transportation demand was 51 trillion passenger-km in 2014, which is expected to more than double and reach 110 trillion passenger-km in 2060 [1].Most of the increase is expected to emerge in developing countries where the population and economy are growing significantly. Other studies suggest that a combination of various transportation demand management measures, including public transport provision [7], pricing [8], information service [9], coordination with land use [10,11], and active transport promotion [12,13], are needed to tackle congestion. The use of smart technologies in the transport sector has been investigated intensively, such as bike-sharing [12,13], application of big data [14,15,16], implementation of mobility as a service platform and information
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