Abstract Carpooling is a sustainable and ecologically acceptable transportation mode. Individuals commonly engage in coordination and negotiation processes to find matching partners and typically modify their schedules to enable cooperation. Mutual cooperation between carpooling individuals plays an important role in executing trips. Through cooperation, participants can achieve challenging agreements effectively in a repetitive manner. This paper presents a negotiation mechanism that can match individuals for carpooling using organization and agent-based concepts. It describes a matching model and a carpooling social network. It studies several aspects of multi-zonal individual behavior to identify groups of carpooling candidates. The carpooling social network is simulated on an ongoing basis for each of the following carpooling activities: interaction, negotiation, and trip execution. The interaction process enables communication between individuals within carpooling social groups in order to activate the negotiation process. During the negotiation process, participants typically modify their schedules to support cooperation by considering their personal preferences and constraints. Negotiation leads to matching of individuals based on trip start times, driver selection, detour duration, and carpool group pickup and dropoff sequences. Trip start times are established on travel, social, financial, and schedule-related factors. The carpoolers’ pickup and dropoff sequences that are feasible for an optimal carpool group are projected using specific scoring methods. Carpooling community candidates are recognized via outcomes projected using the FEATHERS activity–based model. The framework is implemented through the Janus multi-agent system.

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.

In Flanders (Belgium), Mobility impaired people need to travel frequently from their homes to a Day Care Centre (DCC). Currently this is done by subsidised bus services but recently a decision was made to cancel these subsidies. The fare the DCC guests will have to pay for transport by bus is too high for most of them. This paper investigates a solution where voluntary drivers bring as many DCC guests as possible to the DCC by carpooling. These drivers can pick up and drop off DCC guests along the way to their work location or any other destination. In general it turns out to be impossible to drive all the DCC guests to the DCC by carpooling. The remaining DCC guests will be picked up by dedicated buses. The goal is to keep the bus travel cost as low as possible. The solution is constrained by car capacities, time windows for both drivers and DCC guests, upper bounds for detours and the availability of intermediate transfer locations. The main challenge is the involvement of multiple transportation service providers. Some of these are not under the control of the consultant in charge of finding an efficient solution for the DCC and hence, their operation and cost cannot be included in the objective function. Solving the problem requires consideration of several cases each leading to a heavy combinatorial computation. Although it seems to be impossible to find a carpool solution in which all the passengers reach the DCC, the results are promising. In several cases four or more chartered buses can be saved on. However, average results show a saving around one to two chartered buses which represents a cost reduction between 20% and 30%.

Activity-based travel demand modeling is the approach with most relation and need for intelligent solutions as it directly aims at reproducing human decision making in daily life. Therefore, the way to implement the selected intelligent solution plays an important role in the successful application of the models. FEATHERS (the Forecasting 16 Evolutionary Activity-Travel of Households and their Environmental RepercussionS) is an activity-based microsimulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. For the sake of more detailed travel demand forecasting, we investigated in this study the extension of the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems was compared. The extended modeling system was further applied to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders.

Carpooling for commuting can save cost and helps in reducing pollution. An automatic Web based Global CarPooling Matching Service (GCPMS) for matching commuting trips has been designed. The service supports carpooling candidates by supplying advice during their exploration for potential partners. Such services collect data about the candidates, and base their advice for each pair of trips to be combined, on an estimate of the probability for successful negotiation between the candidates to carpool. The probability values are calculated by a learning mechanism using, on one hand, the registered person and trip characteristics, and on the other hand, the negotiation feedback. The problem of maximizing the expected value of carpooling negotiation success was formulated and was proved to be NP-hard. In addition, the network characteristics for a realistic case have been analyzed. The carpooling network was established using results predicted by the operational FEATHERS activity based model for Flanders (Belgium).