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This paper proposes a framework to extract dynamic trip flows and travel demand patterns from large-scale 2 G and 3 G cellular signaling data. Novel data pre-processing techniques based on cell phone activity metrics are presented. The trip extraction method relies on the detection of stationary activities to form trip sequences related to resident users. A probabilistic solution is introduced to estimate the trip starting time, allowing to aggregate trips by time of the day and reconstruct hourly travel flows. To better characterize these flows, a spatial clustering process combined with land-use data is proposed based on the temporal demand profile of each zone. Empirical comparisons have been performed showing that the resulting dynamic travel demand patterns are consistent with those obtained from travel survey data with high correlation coefficients of about 0.9. The results prove the potential of signaling data to generate low-cost valuable information for large-scale travel demand modeling. [ABSTRACT FROM AUTHOR]

Matching potential carpool partners in large companies is one of the critical tools to establish carpooling. The main practical problem of every matching framework is that it only starts working well when a sufficiently large set of candidates is available. For this reason, public carpool matchers have fairly low success. This article presents the components and development decisions for a closed group framework for matching employees who are candidates for carpooling. It is designed to be operated by employers in order to find optimal carpool matching solutions which are to be proposed to candidate carpoolers. It has the capability to account for dynamic evolution of the extracted personnel database in order to minimize burden on the users. This framework is capable to match candidates based on their home and target locations, time windows, allowed detour durations and several attributes describing personal behavioral properties specified by the interested candidates. People fix their choice after negotiation within small groups and feedback their decision to the system that maintains a carpool calendar and personal preferences for every participant. The result is a dynamic system that evolves to a user optimum (as opposed to system optimum) and therefore can be considered as stable. As a proof-of-concept, experiments were conducted at the scale of the Doppahuis database. The matching framework is sufficiently efficient to recompute the advice to the customers after changes in the carpooling candidates’ database and due to unexpected changes in daily travel requirements. Results show that the computation time of the framework grows in a polynomial way with the scale of the potential carpoolers in a carpool. [ABSTRACT FROM AUTHOR]

This paper presents descriptive analyses to investigate the transferability of FEATHERS, an activity-based travel demand model developed for Flanders (Belgium), a region in a developed country to Ho Chi Minh City (Vietnam), a metropolitan area of a developing country using personal and household travel survey data. The transferability was tested by using indicators which represent individuals' activity and travel behaviors. The results confirm the transferability of FEATHERS modelling structure to Ho Chi Minh City (HCMC), but all sub-models require recalibration due to the different cultural settings between the two study areas. The differences in transport mode options, land use patterns and individuals' location choice preferences make transport mode and location choice the least transferable models. [ABSTRACT FROM AUTHOR]