01661302

Component

The increased demand for targeted and effective transport policies put forward the need for detailed methods and models for analysing mobility. Additionally, the emergence of new types, sources and channels of information, directly or indirectly related to peoples’ mobility, require new methodological means both for handling them, but most importantly, for extracting and make use of the detailed information that previously was not able to be collected. For example, nowadays information about peoples’ activities, habits and schedule can be possibly collected and estimated from various sources, like cellular mobile data, social networks activities, electronic ticketing and other. Such data could be reliably utilized on analytical activity-based models able to represent mobility patterns in very detailed, methodologically sound and analytical manner, valuable for in-depth transportation policy design. On the other hand, the type of data that authorities and agencies have collected over the years, developed, maintained and relied on for many purposes, correspond to standard supply and demand information. In the current paper, a bridging of standard demand records, namely peak-hour and off-peak-hour origin-destination (OD) matrices, to trip chains such as to reflect series of activities is proposed. The resulted trip chains are then fed into a well-documented autonomous agents activity-based network model, namely MATSim, such as to investigate the performance of the proposed synthetic activities records. The proposed approach is tested on a realistic urban network, that of Nicosia, capital of Cyprus and the results are provided and discussed in detail. Although the proposed approach cannot replace a thorough activities data collection procedure, though it can be regarded as a useful approach for extrapolating standard ODs for dynamic to activity-based modelling and a reasonable approximation for preliminary analysis.

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

18-04895

English

14p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Activity choices; Data collection; Mobility; Origin and destination; Peak periods; Transportation policy; Trip chaining

Planning and Forecasting; Policy; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-04895

TRIS, TRB, ATRI

Jan 8 2018 11:12AM