Search

With the introduction of smart cards for electronic payment in public transport, massive spatio-temporal data have been recorded in many cities. This data represents how public transport users from a whole city or country travel during consecutive days or weeks. This work presents a method to identify temporal weekly patterns of primary activities performed by public transport users in Singapore. As continuous travel data from the same user during a week can be extracted, work-leisure cycles can be recognized. Using revealed trips from a 1% sample household travel survey, activities reached and left by public transport were extracted and used to develop two discrete choice models: one for workers and one for students. According to the start time and duration of an activity, these models estimate the likelihood of such activity to be HOME, WORK/STUDY or OTHER type. Then, consistent activities were extracted from public transport smart card transactions recorded during one week. Using the discrete choice models, these extracted activities were classified by type. For each user, a 14-dimension vector summarizes his/her primary activity pattern. Each vector is composed by start time and duration of his/her work or study activities during continuous 7 days. To recognize the most common primary activity patterns of the city, the DBSCAN clustering algorithm was applied to these vectors. As the input data of this method is commonly available, the method can be applied to many cities in the world.

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.

In the planning and evaluation stages of infrastructure projects, it is necessary to manage huge quantities of information. Cities are very complex systems, which need to be modeled when an intervention is required. Suchmodels allow us to measure the impact of infrastructure changes, simulating hypothetic scenarios and evaluating results. This paper describes a methodology for the automatic generation of urban space models from GIS sources. A Voronoi diagram is used to partition large urban regions and subsequently define zones of interest. Finally, some examples of application models are presented, one used for microsimulation of traffic and another for air pollution simulation.En las etapas de planeación y evaluación de proyectos de infraestructura es necesario manejar grandes cantidades de información. Las ciudades son sistemas complejos que deben ser modeladas para ser intervenidas. Estos modelos permitirón medir el impacto de los cambios de infraestructura, simular escenarios hipotéticos y evaluar resultados. Este artículo describe una metodología para generar automáticamente modelos espaciales urbanos desde fuentes SIG: Un diagrama de Voronoi es usado para dividir grandes regiones urbanas, y a continuación serán definidas las zonas de interés. Finalmente, algunos ejemplos de modelos de aplicación serán presentados, uno usado para microsimulación de tráfico y el otro para simular contaminación atmosférica.