The number and the temporal and spatial distribution of work locations are crucial information for any transport demand model. To generate the initial transport demand of MATSim, an activity-based multiagent simulation framework, it is necessary to determine dynamic workplace capacities with high spatial resolution, either on a parcel or even a building level. Commonly applied methods to derive work locations are based on census of enterprises information, unemployment insurance database, or combined information of a building's gross floor area and individual work space requirements. As an alternative, the authors present a methodology that combines public transport smart card transaction data, travel diary surveys, and building information data sources. Work activities are detected from smart card transactions based on observed activity duration and start time and therefore related to public transport stops. To link the observed work activities to individual buildings, a linear programming optimization technique is applied that minimizes the walking time between public transport stops and potential work locations. The method classifies work activities in representative work schedules obtained by clustering methods. Information on maximum allowed building gross floor area derived from land use regulation is combined with estimates on individual work space requirements to ensure that buildings are only assigned with work activities according to their maximal capacity. To account for private transport based work activities, mode shares as observed in a travel diary are taken into account. To demonstrate the applicability, the proposed approach is implemented in Singapore and the results critically reviewed.
Estimating Dynamic Workplace Capacities by Means of Public Transport Smart Card Data and Household Travel Survey in Singapore
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2344 , 1 ; 20-30
2013-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch