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Electrification of the road transportation sector is one of the ways to reduce the green-house gases (GHGs) emissions worldwide, but an increasing number of electric vehicles (EVs), will impact on the power system, particularly on the residential distribution low-voltage grid. In order to reduce this impact during the peak periods, it is possible for these vehicles to get energy not only from the grid, but also from other EVs parked at the same time in the same place through a peer to peer (P2P) energy trading. In this paper a night charging method that optimizes the recharging process of an EV fleet at regional level depending on hourly energy price in a P2P energy trading system is presented. This algorithm determines how much energy should be recharged in the battery of each EV and the corresponding time slot to do it, avoiding the discontinuities in the charging process and considering the users’ personal mobility constraints.

Investigation of the local effects of travel demand and the study of thin flows (low volume travel demand) require micro-modelling and simulation. The ongoing Smart-PT project focuses on demand responsive public transport and other collective transportation services; the Leuven region is used as a research case. This paper reports on the first steps in the project. Those steps aim to model the large number of trips generated by the academic hospital in the moderately sized city. The hospital attracts patients and their visitors from all over Flanders. The method used to sample them from a synthetic population is described and first results are reported. The daily agendas for the synthetic individuals are generated by the FEATHERS activity-based model. The next steps consisting of schedule adaptation for hospital personnel and visitors are briefly discussed. Those are non-trivial but essential to support the micro modelling required to study local public and collective travel demand.

This paper presents the cost optimization model which plans a charging strategy for an electric vehicle. In case of time dependent electric prices an intelligent planner is required which plans the charging strategy only at cheaper moments and places to keep the vehicle charged enough to complete its scheduled travels. This model estimates the required charging energy to travel by the electric vehicle. Then using the time dependent electric prices and available power at each period of the time suggests a charging pattern for the electric vehicle which ensures the cheapest charging cost and fulfills the constraints of battery state of the charge. According to the current market share of electric vehicles, a fraction of daily agendas created by the large scale activity-based model are used to test the proposed framework. A central power tracker is introduced which keeps track of available and required power at each period of the day. It also manages the charging requests from electric vehicles. Moreover, an experiment has been set up, it makes use of wind and solar energy production data. Price signal is derived from available power as an indicator of relative cost. The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement nr 270833.

The aim of this research is to identify the relationship between travel mode and trip (purpose, time, distance) and person related factors (gender, age, diploma, attitude towards electric mobility). The data for this study were collected through the Flemish Electric Living Labs (Belgium-Flanders). Within this context participants were able to integrate an electric vehicle in their daily lives over a period of ten weeks. In return the test population had to fill in a travel diary for two weeks. To estimate travel mode a discrete choice model, via a multinomial logistic regression was analysed. All pre-defined factors influence and hence predict in a certain way travel mode choice. Especially trip distance is determinative: from short to long distance, drivers are likely to first choose a bike (or going by foot), followed by an electric vehicle and thereafter a conventional car. The predictive power of 'attitude towards electric mobility' is investigated in this paper. A positive attitude has a plausible connection with a greater concern for the environment and hence an explanation why drivers are more likely to opt for the bike (or going by foot) instead of taking an electric vehicle.

In order to commute by carpooling, individuals need to communicate, negotiate and coordinate, and in most cases adapt their agenda (daily schedule) to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way which meet the criteria for the successful negotiation. The procedure of negotiation and trip execution in the carpooling consists of a number of steps namely; (i) explore the social network, (ii) negotiate agenda to reach complex agreements, and (iii) long term trip execution. This paper presents a conceptual design of an organizational-based and agent-based model (ABM) of a set of candidate carpoolers that serves as a proof of concept and is an extension of a simple negotiation model for carpooling. The proposed model is used for simulating the interactions of autonomous agents with their agenda and explore social networks to initiate agent communication to trigger the negotiation process. The schedule adaptation depends on the preferences among feasible schedules of the individuals, generally depends on both the time of day and on the duration of the participation. In this simulation of the evolution we consider a daily schedule that consists of three activities (home, work and home) and a chain of two intermediate trips (home-to-work and work-to-home) only. We carried out a validation study of our results with real data collected in Flanders, Belgium. From the simulation’s discussions, it is possible to understand the causes why people can adapt their daily schedule to enable cooperation in order to carpool. The future research will mainly focus on enhancing the mechanism for negotiation between agents.

Although, in the carpooling process, effective negotiation requires that individuals (agents) effectively convey and interpret information to enable carpooling. However, the strict timing constraints in the schedule of the day have the opposite effect. Through negotiation, individuals can reach complex agreements in an iterative way. This paper presents the design of an agent-based model to analyze various effects of agent interaction and behavior adaptation of a set of candidate carpoolers that serves as a proof of concept. The proposed model extends the previous one by applying different cases for the constraining activities for their work trip start times. The start of the carpooling process depends on the individuals’ objectives and intention to carpool. From the simulation’s discussions, it is possible to portray the real picture of the potential carpoolers throughout their carpooling period. The Janus (multi-agent) platform is used for simulating the interactions of autonomous agents with their agenda.

This paper presents a novel vehicle to vehicle energy exchange market (V2VEE) between electric vehicles (EVs) for decreasing the energy cost to be paid by some users whose EVs must be recharged during the day to fulfil their daily scheduled trips and also reducing the impact of charging on the electric grid. EVs with excess of energy in their batteries can transfer this energy among other EVs which need charge during their daily trips. These second type of owners can buy the energy directly to the electric grid or they can buy the energy from other EV at lower price. An aggregator is responsible for collecting all information among vehicles located in the same area at the same time and make possible this energy transfer.

The concept of rescheduling is essential to activity-based modeling in order to calculate effects of both unexpected incidents and adaptation of individuals to traffic demand management measures. When collaboration between individuals is involved or timetable based public transportation modes are chosen, rescheduling becomes complex. This paper describes a new framework to investigate algorithms for rescheduling on a large scale. The framework explicitely models the information flow between traffic information services and travelers. It combines macroscopic traffic assignment with microscopic simulation of agents adapting their schedules. Perception filtering is introduced to allow for traveler specific interpretation of perceived macroscopic data and information going unnoticed; it feeds person specific short term predictions required for schedule adaptation. Individuals are assumed to maximize schedule utility. Initial agendas are created by the FEATHERS activity-based schedule generator for mutually independent individuals using an undisturbed loaded transportation network. The new framework allows both agent behavior and external phenomena to influence the transportation network state; individuals interpret the state changes via perception filtering and start adapting their schedules; again affecting the network via updated traffic demand. The first rescheduler investigated uses marginal utility that monotonically decreases with activity duration and a monotonically converging relaxation algorithm to efficiently determine the new activity timing. The current framework implementation can support re-timing, re-location and activity re-sequencing; re-routing however is the subject of future research.

Electric power demand for household generated traffic is estimated as a function of time and space for the region of Flanders. An activity-based model is used to predict traffic demand. Electric vehicle (EV) type and charger characteristics are determined on the basis of car ownership and by assuming that EV categories market shares will be similar to the current ones for internal combustion engine vehicles (ICEV) published in government statistics. Charging opportunities at home and work locations are derived from the predicted schedules and by estimating the possibility to charge at work. Simulations are run for several EV market penetration levels and for specific BEV/PHEV (battery-only/pluggable hybrid) ratios. A single car is used to drive all trips in a daily schedule. Most of the Flemish schedules can be driven entirely by a BEV even after reducing published range values to account for range anxiety and for the over-estimated ranges resulting from tests according to standards. The current low tariff electricity period overnight is found to be sufficiently long to allow for individual cost optimizing while peak shaving overall power demand.

Carpooling enables commuters to share travel expenses, save costs, and improve their mobility options and reduces emission and traffic congestion. To commute by carpooling, individuals need to communicate, negotiate, and coordinate, and in most cases they need to adapt their schedule to enable cooperation. This paper presents the design of an agent-based model by defining phases and steps that may be taken to move from solo driving to carpooling. The paper analyzes the various effects of agent interaction and behavior adaptation for a set of candidate carpoolers. The start of the carpooling process depends on the individuals’ objectives and intention to carpool. Through negotiation and coordination, individuals can reach complex agreements in an iterative way. The success of negotiation highly depends on the lifestyle factors that influence the departure time decision, on the profile of the individuals, and on the effect of constraining activities. The carpooling social network was established by use of the results predicted by FEATHERS, an operational activity-based model for Flanders, Belgium. From the simulation’s discussions, it is possible to portray the true picture of potential carpoolers throughout their carpooling period. The simulation results show that 9.33% of the commuters started to carpool when the time window was ±30 min and the average occupancy per car was 2.4 persons. When the time window was larger, the chances for negotiation success were greater than those when a smaller time window was used. Hence, carpooling requires time flexibility. The Janus (multiagent) platform was used to simulate the interactions of autonomous agents.

When simulating individuals' daily plan, lots of unexpected events need to be considered, like traffic jam and weather changes. Therefore, there will be a mismatch between the original plan and the executed one. Faced with this situation, individuals need to adjust the rest of the activities to make a new schedule. This paper analyzes the causes of rescheduling, and establishes a new rescheduling model, combining strengths of existing rescheduling models. The model in this paper considers the rescheduling possibilities and choices as much as possible. It takes time pressure and schedule similarity into consideration when updating a schedule. Furthermore, this paper analyzes joint trip/activity execution by studying the cooperation between agents during the rescheduling process. (C) 2018 The Authors. Published by Elsevier B.V.

Demand responsive collective transportation might be a solution to serve thin flows that occur when the average demand per time unit for travel between particular locations is small. Small capacity and/or specially equipped vehicles are deployed to serve low population density areas and mobility impaired passengers. The variability of the demand and the low vehicle capacity require daily optimal planning of routes. The cost for daily tours heavily depends on the temporal and spatial distribution of the demand. This paper proposes a co-simulation model to evaluate the profitability of thin flow service providers over multiple years of operation under specific condition of public compensation (subsidizing). The research reported was partially co-funded within ERA-NET Transport III Flagship Call 2013 “Future Traveling”, project: “Smart-PT - Smart Adaptive Public Transport”, under grant IWT 135026 (Belgium) and NCBR ERA-NET-TRANSPORT-III/2/2014 (Poland).