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Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam. [ABSTRACT FROM AUTHOR]

Activity-based models predict locations at TAZ (travel analysis zone) level. Recent research questions about carpooling, ride sharing, vehicle sharing, multi-modality, MaaS,... require space resolution at street address level. Pitfalls related to spatial resolution adaptation are identified and a disaggregation method beyond sampling from uniform distributions is presented. Daily travel plans generated by the FEATHERS activity based model for The Netherlands are presented and compared to observations. [ABSTRACT FROM AUTHOR]

The decision to own a car usually results from a mix of changes in socio-economics, residential location, demographics or preferences of the individuals. These changes do not directly influence car ownership but are lagged in time or sometimes occur on anticipation of future events. In this research we investigate how some of the most common predictors for car ownership change over the years and how much impact they actually have in their decision for owning a car. We make a distinction in impact for people living in an urban and people living in a rural environment. A multinomial logit was estimated in 4 separate years to assess the impact of changes in residential location, age and income. The results show that not only the impact of these common predictors is changing but also the relation that they have regarding car ownership. These results illustrates the complexity of car ownership and suggests a more dynamic approach for the prediction of car ownership in both urban and rural environments. [ABSTRACT FROM AUTHOR]

MATSim is an agent-based transport simulation model. In contrast to a pure dynamic traffic assignment (DTA) model, MATSim can react to more choice dimensions than route choice, which is the only choice treated by a typical DTA. Simulating the mobility and activity participation of individuals during the whole day, MATSim can additionally represent mode choice, departure time choice, and other decisions and is, therefore, policy-sensitive in terms of these choice dimensions. This allows for the analysis of a wide scope of policies. MATSim can, however, not model the choice of the sequence of activity participation nor the choice of activity participation as such. Also, choices of locations are not typically part of the modeling scope. Interventions into the transport and land-use systems may, however, be substantial such that they can effect changes in behavior in terms of these choices. To allow for the assessment of such reactions, the FEATHERS activity-based demand model is coupled with MATSim. This paper explores different options of integration and describes the development steps of the integration of FEATHERS and MATSim. [ABSTRACT FROM AUTHOR]

Travel demand for the Metropolitan region Rotterdam - The Hague is predicted using a novel tool chain. Non-classical tools are required to cope with the situation where parking supply is reduced, hubs for trip chaining are made operational and people start to use MaaS including new mobility concepts. An activity based travel plan predictor is combined with a dedicated access-egress model and assignment models. Because the activity based model and access-egress model simulate individuals, they can deal with mode switches at hubs while considering constraints with respect to vehicle ownership, mode availability in the tour, locations where vehicles should be returned and mode availability. Parking capacities, hub and new mobility concepts are included in the assignment models to generated level-of-service matrices that are input to the other models. The tool chain setup, the methods applied, the datasets used and first results are discussed. The new integrated activity based approach proved to be better suited to model impacts of parking, hubs and new mobility concepts than the standard aggregated modelling approach. The results for the base year look promising because they closely resemble observed schedules and the elasticities are within the recommended ranges mentioned in literature. [ABSTRACT FROM AUTHOR]

Cities in the Netherlands have encouraged cycling in order to create a more healthy, liveable and sustainable environment. Accordingly, cycling has become an important travel mode in cities for both unimodal and multimodal travel. Consequently, the increase of bicycle use results in an increase in the demand for bicycle parking, thus encouraging illegal bicycle in station areas where supply is unable to meet demand. As space becomes scarce in these areas, managing the existing parking supply becomes crucial in the urban environment. This research attempts to explain bicycle parking behavior by finding determinants for parking near a station with a metro service, train service or both services at the same location. The results not only show that the determinants for parking in these station areas differ, but also that each station areas attracts different groups of people. [ABSTRACT FROM AUTHOR]

Microsimulation of travel flows aims to assess the effect of decisions taken by travelers based on personal preferences, time-of-day, properties of the infrastructure and expected or perceived travel flows. Route choice represents a particular class of such decisions. Route choice prediction is an essential component of microsimulators. Specification of choice models and estimation of the corresponding parameters based on observations are required in the preparatory stage. Route choice sets need to be established for sampling in the simulation stage. This paper is part of a research project aiming to investigate how route complexity can be integrated in the choice process modeling. In particular routes for bikers collected by GPS tracking in the Dutch FietsTelWeek project in 2016 are analyzed. The data exploration stage and the research project outline are covered. Properties of the publicly available fietstelweek2016 dataset used for model training are investigated in order to assess their effect on prediction results. In order to achieve the project goal, the research project structure is briefly discussed. It is based on the observation that the number of routes recorded for each OD-pair is too small to observe a frequency distribution for complexity. Hence, complexity data are collected for sub-networks that are similar with respect to particular graph properties. [ABSTRACT FROM AUTHOR]

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which is considered to be important for route choice in a similar way as the number of left turns, the number of speed bumps, distance and other. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions seem to significantly differ so that the choice sets do not reflect the traveler preferences. This paper looks at how the observed routes compare to routes generated by Breadth First Search Link Elimination and Double Stochastic Generation Function method. [ABSTRACT FROM AUTHOR]

Public transit is a backbone for well-functioning cities, forming a complicated system of interconnecting lines each with their own frequency. Defining accessibility for public transit is just as complicated, as travel times can change every minute depending on location and departure time. With Pareto optimal journeys it is possible to look beyond the earliest arrival times and also optimize for the shortest travel time, as travellers base their departure time on the start time given by their smartphone app, especially when service frequencies are low. By querying for all Pareto optimal journeys in a time range it becomes possible to get a grasp of what passengers see as their choice set when it comes to transit route choice. Based on the averages of the Pareto optimal journeys it should become possible to calculate more realistic skim matrices for traffic analysis zones, including reliability factors such as frequencies and the number of transfers. In this study we calculate Pareto optimal journeys in the area in and around Amsterdam, looking at how travel times are distributed and what factors impact them. [ABSTRACT FROM AUTHOR]

A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim is to use both datasets to determine an optimal action plan for problem solving given a limited budget. First, problematic locations are clustered. Each cluster corresponds to an impediment. Impediments are then associated with trips using a distance function. The aim is to find out which impediments to solve under a given budget constraint in order to maximize the number of impediment free trips. Thereto the trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments in such set induces a cost and makes the associated part of trips impediment free. An optimizer is presented and evaluated. [ABSTRACT FROM AUTHOR]

As an alternative to the car, the bicycle is considered important for obtaining more sustainable urban transport. The bicycle has many positive effects; however, bicyclists are more vulnerable than users of other transport modes, and the number of bicycle related injuries and fatalities are too high. We present a clustering analysis aiming to support the identification of the locations of bicyclists' perceived unsafety in an urban traffic network, so-called bicycle impediments. In particular, we used an iterative k-means clustering approach, which is a contribution of the current paper, and DBSCAN. In contrast to standard k-means clustering, our iterative k-means clustering approach enables to remove outliers from the data set. In our study, we used data collected by bicyclists travelling in the city of Lund, Sweden, where each data point defines a location and time of a bicyclist's perceived unsafety. The results of our study show that 1) clustering is a useful approach in order to support the identification of perceived unsafe locations for bicyclists in an urban traffic network and 2) it might be beneficial to combine different types of clustering to support the identification process. [ABSTRACT FROM AUTHOR]

Microsimulation of travel flows aims to assess the effect of decisions taken by travelers based on personal preferences, time-of-day, properties of the infrastructure and expected or perceived travel flows. Route choice represents a particular class of such decisions. Route choice prediction is an essential component of microsimulators. Specification of choice models and estimation of the corresponding parameters based on observations are required in the preparatory stage. Route choice sets need to be established for sampling in the simulation stage. This paper is part of a research project aiming to investigate how route complexity can be integrated in the choice process modeling. In particular routes for bikers collected by GPS tracking in the Dutch FietsTelWeek project in 2016 are analyzed. The data exploration stage and the research project outline are covered. Properties of the publicly available fietstelweek2016 dataset used for model training are investigated in order to assess their effect on prediction results. In order to achieve the project goal, the research project structure is briefly discussed. It is based on the observation that the number of routes recorded for each OD-pair is too small to observe a frequency distribution for complexity. Hence, complexity data are collected for sub-networks that are similar with respect to particular graph properties. [ABSTRACT FROM AUTHOR]

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which is considered to be important for route choice in a similar way as the number of left turns, the number of speed bumps, distance and other. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions seem to significantly differ so that the choice sets do not reflect the traveler preferences. This paper looks at how the observed routes compare to routes generated by Breadth First Search Link Elimination and Double Stochastic Generation Function method. [ABSTRACT FROM AUTHOR]

This paper describes the design of SimPark, an agent-based micro-simulator with state-of-the-art parking components. The goal is to allow the simulation of parking policies and strategies while capturing their effects on traffic flow and personal schedules. Though individuals’ parking strategies are an important part of the simulator, they are out of scope in the presented research. All other components of the simulator are discussed in depth and compared with existing parking simulators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Estimation of spatio-temporal travel demand requires accurate activity schedules as an input along with a mechanism to adapt the schedules to changing travel options. Individuals are assumed to own a duty list of activities to be accomplished within the simulated period. A partial order based on chronological and functional constraints determines the set of feasible activity execution sequences (plans). Trip and activity timing is determined by schedule prediction and adaptation. Event times in a schedule are constrained by conditions involving time-of-day (absolute time) and by duration constraints (relative time). Both types of constraints are expressed using time deviation functions (TDF). Each start and end event in a schedule induces a set of non-linear equations expressing the absolute and relative constraints. Time values are determined by solving the set of non-linear equations using a relaxation method. A discrepancy evaluation function is used both as a criterion to decide convergence of the relaxation and to compare alternative schedules for a given plan. [ABSTRACT FROM AUTHOR]

Abstract: Due to budget constraints public transportation (PT) can no longer be deployed in regions where it attracts insufficient customers. Novel techniques like demand-responsive collective transportation (DRT) are evaluated to cut costs. This requires detailed simulations that are able to predict travel demand and include trip execution. Simulating facilities acting as feeder services to time-table based PT services requires detailed and accurate information about the PT infrastructure on a network. However, there are no public data sources that combine network and PT infrastructure data with the preferred level of detail. A newly developed bus stop mapping technique is presented. It uses the OpenStreetMap (OSM) and General Transit Feed Specification (GTFS) open data sources, which are maintained independently. Merging the data into a single database requires alignment. Developing bus stop mapping algorithms is challenging due to (i) inaccurate location data, (ii) inconsistent data sources and (iii) the vastly interconnected PT network and services. Due to the inaccuracy in the GTFS stop locations and in the OSM network, pure geometric considerations might lead to multiple candidate solutions to map a stop to the network. The new technique handles all GTFS trips at once and operates under the assumption that PT operators minimize the total distance driven to complete all trips. [ABSTRACT FROM AUTHOR]

Abstract: 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). [ABSTRACT FROM AUTHOR]

Abstract: This paper concerns the structure of movements as were recorded by GPS traces and converted to routes by map matching. Each route in a transportation network corresponds to a collection of directed paths or cycles in a digraph. When considering only directed paths, corresponding to utilitarian trips, the path is not necessarily a shortest path between its origin and destination, and can be split up into a small number of segments, each of which is a shortest or least cost path. Two consecutive segments are separated by split vertices . Split vertices act as intermediate destinations in the mind of travellers who try to hop between them using minimum cost paths. Hence they provide useful information to build route choice models. In this paper we identify and enumerate all possible decompositions of a path into a minimum number of shortest segments. This gives us an indication of the importance of split vertices occurring in particular sets of revealed routes that belong either to a single traveller or to a specific group. The proposed technique allows for automatic extraction of frequently used intermediate destinations (way-points) from revealed preference data. [ABSTRACT FROM AUTHOR]

Electric vehicles can be sustainable alternative in contrast to conventional fossil fuel powered vehicles only if the green energy is used to power them. Without coordination among electric vehicles and grid operator, it can imbalance the power production and demand. This paper presents an automated coordinated mechanism among EV fleet and the grid operator that plans a charging strategy for electric vehicles while sustaining the grid capacity constraints. The intelligent planner plans the charging strategy at the cheaper moments and keep the vehicle charged enough to complete its scheduled trips It suggests a charging pattern for the electric vehicle by using the time dependent electric prices and available power at the given time slots. It also ensures the cheapest charging cost and fulfills the constraints of battery state of the charge. A central power tracker is also introduced which keeps track of the available and required power at each time slot. According to the current market share of the electric vehicles, a fraction of the daily agendas, created by an operational activity-based model, is used to test the framework. Moreover, an experiment has been set up, it makes use of wind and solar renewable energy to power the vehicles. [ABSTRACT FROM AUTHOR]

Many activity-based models start by scheduling inflexible or mandatory activities (if present), before more flexible activities. Often work and educational activities are assumed as most stringent and recognized as the only mandatory activities. According to this definition, only 45% of all schedules contains a mandatory activity (OVG single-day travel survey in Flanders, Belgium). This means 55% of schedules does not have a traditional mandatory-flexible activity structure. This research proposes a completely data-driven approach to reveal the real basic structure of individuals’ schedules, i.e. the skeleton schedule sequence. To this end, a sequential clustering algorithm was developed. Furthermore, an in-depth analysis of the parameter settings was performed. The proposed method reveals a set of skeleton activity schedules and confirms the importance of work and education. [ABSTRACT FROM AUTHOR]

Traffic congestion in mega cities is a common phenomenon for developing countries. Numerous studies on congestion cost estimation, that aim to quantify their monetary losses, have been conducted. Value of Time (VOT) assessment through utility maximizing theory and choice models are abundantly applied in transport literature. However, estimating VOT on congested network is not widely applied yet. To recognize the difference under normal and congested network, the current study focuses on VOT estimation for work trips in an extremely congested network.The focus of this research is to conduct a VOT estimation of the National Highway, Karachi. It connects Karachi city with Port Qasim Industrial area and the rest of the country. A large amount of freight transport to and from the port is also observed on this road. The National highway, being the only link to commute to this industrial area, is therefore under excessive traffic congestion.A stated preference (SP) survey was conducted at various industries located in this stretch. The respondents were asked about current travel practices and their (stated) preferences based on hypothetical -though realistic- travel attributes. A choice set of four alternative modes based on the currently used mode was presented to each individual. A Multinomial Logistics Regression (MNL) Model was developed for data analysis.As perceived, the results revealed a strong impact of travel time and travel cost on the (dis)utility of travel. These results can be utilized by policy makers to reduce congestion, monetary and time losses through efficient transport planning. [ABSTRACT FROM AUTHOR]

Research on demand-responsive collective transportation facilities that can act as feeder services to time-table based public transportation (PT) requires detailed and accurate information about the PT infrastructure, including the attachment of bus stops to the appropriate network link. Due to the size of the infrastructure, the data integration shall be automated. This paper describes the effort to prepare data from publicly available OpenStreetMap (OSM) and General Transit Feed Specification (GTFS) sources. Procedures are proposed (i) to build a network derived from OSM suitable for simulations in transportation, (ii) to extract bus stops from GTFS and remove anomalies and (iii) to find candidate network links to attach them. [ABSTRACT FROM AUTHOR]

A multitude of studies have been motivated on the association between land use, urban settings and transport infrastructure to assist policy makers in sustainable planning. Alike, incorporation of cross-border trips have been an integral part of transportation demand models through external surveys. The present study seeks to explore the Incoming Cross Border Traffic (ICBT) into a study area based on the characteristics of a study area that attracts cross-border trips from outside region. This paper presents an analysis of cross-border trips in Karachi Metropolis, largest city of Pakistan, through Household Individual Survey (HIS-2010) and land use data from alternative resources. Results reveal that land use particulars, socioeconomic characteristics and travel attributes of individuals significantly influences cross-border trips and this effect varies spatially. Work, shopping and Education trips are discussed through separate models in this paper with a number of practical insights to policy makers for sustainable development of city. This study contribute in elucidating travel behaviour through land use parameters and also persuade professionals to integrate estimation of cross-border trips by socioeconomic parameters, in transport forecasting models. [ABSTRACT FROM AUTHOR]

The decision about spending time on an activity, switching to the next activity and transport mode used to travel to the next activity location depends on money value of time; opportunity cost of time at activity. Optimal condition of transition between two activities occurs when their marginal utility of time is equal. The presented framework in this paper models the marginal utility of activity to express the money benefit earned by spending each unit of time at the given activity. The proposed model is generalized for the schedule with any number of activities as contrast to previous studies, where such models were used for schedules with fixed number of activities. This framework can be used to calculate the loss in value of time due decreased activity participation resulting from travel delays. [ABSTRACT FROM AUTHOR]

Low volume traveler flows cause problems for public transportation (PT) providers. The Smart-PT project aims to find out how such flows can be combined to increase the service provider viability. The capability to conceive multi-modal trips is fundamental in that context and is modeled by the Trip Sequence Composer (TSC) concept. A TSC is an essential component of the traveler's brain, of the customer support operated by collective transport providers, of trip advisers in websites etc. We present a simulation model design to evaluate the effect of cooperating TSCs on the viability of demand responsive collective transport providers. While obeying specific regulations, specialized services targeting mobility impaired people can also serve regular requests in order to save fleet and personnel costs. All stakeholders are assumed to optimize their private objectives and none of them has global perfect knowledge. [ABSTRACT FROM AUTHOR]

In order to commute by carpooling, individuals need to communicate, negotiate and coordinate, and in most cases adapt their daily schedule to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way, which meets the criteria for the successful negotiation. The procedure of negotiation and trip execution in the long-term carpooling consists of a number of steps namely; (i) decision to carpool, (ii) exploration and communication, (iii) negotiation, (iv) coordination and schedule adaptation, (v) long term trip execution (carpooling), (vi) negotiation during carpooling and (vii) carpool termination and exploration for new carpool. This paper presents a conceptual design of an agent-based model (ABM) of a set of candidate carpoolers. A proof of concept implementation is presented. The proposed model is used for simulating the interactions between autonomous agents. The model enables communication to trigger the negotiation process; it measures the effect of pick-drop and shopping activities on the carpooling trips. Carpooling for commuting is simulated: we consider a set of two intermediate trips (home-to-work and work-to-home) for the long-term carpooling. Schedule adaptation during negotiation depends on personal preferences. Trip timing and duration are crucial factors. We carried out a validation study of our results with real data (partial) collected in Flanders, Belgium. Simulation results show the effect of constraining activities on the carpooling trips. The future research will mainly focus on enhancing the mechanisms for communication and negotiation between agents. [ABSTRACT FROM AUTHOR]

This paper presents two methods to extract trips and stops from GPS traces: the first one focuses on periods of non-movement (stops) and the second one tries to identify the longest periods of movement (trips). In order to assert the quality of both methods, the results are compared to stops and trips identified by the traveler; this was done by means of a visual tool aimed at alignment of manually reported periods in the diary to automatically recorded GPS coordinates. Several quality indicators are presented; they have been evaluated using sensitivity analysis in order to determine the optimal values for the detector's configuration settings. Person traces (as opposed to car traces) were used. Individual specific behavior seems to have a large effect on the optimal values for threshold settings used in both the trip and stop detector algorithms. Accurate detection of stops and trips in GPS traces is vital to prompted recall surveys because those surveys can extend over several weeks. Inaccurate stop detection requires frequent corrections by the users and can cause them to quit. [ABSTRACT FROM AUTHOR]

This study aims to validate an activity-based travel demand model, FEATHERS, using smart-card data which is collected in Seoul, South Korea, and to discuss some limits and challenges in the prediction of public traffic demands. To achieve the goal, global/local trip pattern indices and a hot-spot analysis were applied for the validation test as a comparison method in this study. Using those methods, the public traffic demands predicted by the simulation in the study area were evaluated comparing with ones in the smart-card data. As a result, FEATHERS Seoul shows the enough performance in predicting the global pattern of the public traffic demands, but a low performance in a local pattern, particularly in some areas with a mixture land-use type and/or a frequent public transit. This is because the current model does not handle such a complicate type of land-use and also a multi-modal trip in the simulation process. In conclusion, this study addressed the limits of the current model through the validation test using smart-card data and suggested some solution to the improvement in the specific models As a future work, we will apply smart-card data for the validation of the models operated in FS, such as a location choice model and a trip mode choice model. [ABSTRACT FROM AUTHOR]

Surveying diaries to collect evidence about travel behavior is known to be susceptible to erroneous reporting. Many respondents do not exactly remember details about executed trips and activities. This paper describes a diary collection project aimed at tracking travel behavior in an electric vehicle pilot project. GPS traces corresponding to the collected diaries have been registered. Dedicated interactive software was used to align reported activity and trip timing with the GPS recordings. The difference between each original diary and the corresponding corrected version, has been quantified. The number of modifications as well as the distribution of their magnitudes have been analyzed. Time needed for correction has been recorded. Those results can serve to plan future data collection efforts and lead to specific recommendations to avoid error and data cleaning time. [ABSTRACT FROM AUTHOR]

Charging cost of electric vehicles depends on the selection of the charging strategy when operating ina spatio-temporal electricity pricing market. In such scenario, selection of the charging strategy can be critical in order to keep the charging cost minimum. Furthermore, a coordination of charging strategies is required to prevent the electric grid from overloading during peak demand periods.Hence, a cost optimization model is implementedfor individual travelers in a coordinated context. Individuals minimize their cost and are constrained by power delivery constraints in space and time.The charging strategy optimization applies to the scenario where the electric energy price varies with time and location.When all trips are fixed in time, interesting low cost charging time slots may be unavailable to a particular individual. Hence, in order to charge at such cheap moments, a shift in traveling moments is proposed in this work. Furthermore, a comparison between cost savings and the requiredagenda adaptations also is discussed in the paper. [ABSTRACT FROM AUTHOR]

In the carpooling, individuals need to communicate, negotiate and in most cases adapt their daily schedule to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way which meet the criteria for successful negotiation. The result of the negotiation depends on “negotiation mechanism” used to match and on the behavior of the agents involved in the negotiation process. This paper presents an organizational and agent-based model for commuting by candidate carpoolers using a simple negotiation mechanism aimed at finding an acceptable agreement between agents to carpool. Initially, the agents involved in exploration process, search for their partners via some kind of Agent Communication Language (ACL); after finding potential partners, they start a negotiation to find matched partner to carpool. After having found a good match, the agents can carpool for a specified time period. The agents join the carpool group when the negotiation is successful and leave the carpool group when the agreed time period is expired. Agents can be part of several carpool groups sequentially. The first implementation used home and work locations as well as preferred trip start times and carpool periods determined by uniformly sampling given sets. Furthermore a simplistic negotiation mechanism used roughly to produce possible results for the synthetic data. An automated negotiation model is implemented and validated through simulation. The Janus multi-agent platform is used. Future research will mainly focus on the development of behaviorally sound negotiation mechanism. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) micro-simulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day. Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity. From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours. Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed. [Copyright &y& Elsevier]

Abstract: We address the carpooling problem as a graph-theoretic problem. If the set of drivers is known in advance, then for any car capacity, the problem is equivalent to the assignment problem in bipartite graphs. Otherwise, when we do not know in advance who will drive their vehicle and who will be a passenger, the problem is NP-hard. We devise and implement quick heuristics for both cases, based on graph algorithms, as well as parallel algorithms based on geometric/algebraic approach. We compare between the algorithms on random graphs, as well as on real, very large, data. [Copyright &y& Elsevier]

Abstract: There are multiple ways to split a path in a directed graph into largest sub-paths of minimal cost. All possible splits constitute path partitions of the same size. By calculating two specific path splittings, it is possible to identify subsets of the vertices (splitVer- texSets) that can be used to generate every possible path splitting by taking one vertex from each such subset and connecting the resulting vertices by a least cost path. This is interesting in transportation science when investigating the hypothesis that people build up their route from least cost components. The splitVertexSets can be easily and efficiently derived from big data (GPS recordings). This allows for statistical analysis of structural route characteristics which in turn can support constrained enumera- tion methods for route choice set building. Furthermore, the boundary vertices separating consecutive route parts, are way points having a particular meaning to their user which constitutes relevant information to the transportation analyst. [Copyright &y& Elsevier]

Abstract: Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely; (i) create a motive to carpool, (ii) communicate this motive with other agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans, and (v) provide a feedback to all concerned agents. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. In this paper, we present a conceptual design of an agent-based model (ABM) for the carpooling a that serves as a proof of concept. Our model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our carpooling application, we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm, and a utility function to trigger the negotiation process between agents. We plan to, as a part of the future work, develop a prototype of our agent-based carpooling application based on the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data. [Copyright &y& Elsevier]

Abstract: We have explored to what extent charging electrical vehicles (EVs) can be exploited to stabilize smart grids. Firstly, we discuss the transition to a future with a lot of renewable energy resources. Next, a decentralized coordinated charging schedule for EVs is proposed, taking into account the comfort settings of the consumers and local and temporal flexibility. Based on the vehicle behavior information (trajectories, parking places and duration, etc.) the algorithm assures that all vehicles can follow their planned trajectories and that power constraints on each car park are always met. An advantage of this decentralized coordination algorithm is that the privacy of consumers, including their future trajectory planning, charging controllers, parking duration, etc. are all treated on local processors on board. As a consequence the responsibility for constructing the charging schedules is put only with the vehicle owner. On the other hand, the parking managers need only to be concerned with the network congestion issues. A first application focuses on controlling the power flows at these parking locations and on rescheduling the charging of the electrical vehicles, so that costs are minimized within the comfort settings and within the physical limitations of the charging stations. This coordinated charging is applied on a car fleet of 200 electrical vehicles and 56 parking locations. Trajectories are computed with an activity based model (FEATHERS). In a second application, the imbalance costs are taken into account as well. The main advantage is for the retailer, who can now actively use the flexibility of the charging process to lower his power trading costs. [Copyright &y& Elsevier]

Abstract: An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service (GCPMS) shall advise registered candidates on how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for negotiation success while trying to merge planned trips by carpooling. 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 GCPMS provides advice by maximizing the expected value for negotiation success. This paper describes possible ways to determine the optimal advice and estimates computational scalability using real data for Flanders. [Copyright &y& Elsevier]

Abstract: This study aims to show an ability of Ontology, which is a formal explicit description of concepts in a domain of interest, in an activity-based microsimulation. Thus, an agent-based carpooling application using ontology techniques is presented as a case study with a focus on three functions of the Ontology. First, Ontology facilitates integrating between heterogeneous databases by defining the relationship between their concepts. Second, Ontology verifies the compatibility and consistency between the different angles to combine varied models in a common structure by providing shared knowledge between different domains modelling with the definition of objects and concepts. Lastly, Ontology is useful for modelling agent communication by means of making explicit the parsed message between agents with the shared knowledge. This paper introduces related studies and basic knowledge about using methodologies, and supports an example of using Ontology in an agent-based carpooling simulation. [Copyright &y& Elsevier]

Abstract: In this position paper, we exploit the tools from the realm of graph theory to matching and portioning problems of agent population in an agent-based model for traffic and transportation applications. We take the agent-based carpooling application as an example scenario. The first problem is matching, which concerns finding the optimal pairing among agents. The second problem is partitioning, which is crucial for achieving scalability and for other problems that can be parallelized by separating the passenger population to sub-populations such that the interaction between different sub-populations is minimal. Since in real-life applications the agent population, as well as their preferences, very often change, we also discuss incremental solutions to these problems. [Copyright &y& Elsevier]

Abstract: Carpooling can cut costs and help to solve congestion problems but does not seem to be popular. Behavioral models allow to study the incentives and inhibitors for carpooling and the aggregated effect on the transportation system. In activity based modeling used for travel forecasting, cooperation between actors is important both for schedule planning and revision. Carpooling requires cooperation while commuting which in turn involves co-scheduling and co-routing. The latter requires combinatorial optimization. Agent-based systems used for activity based modeling, contain large amounts of agents. The agent model requires helper algorithms that deliver high quality solutions to embedded optimisation problems using a small amount of resources. Those algorithms are invoked thousands of times during agent society evolution and schedule execution simulation. Solution quality shall be sufficient in order to guarantee realistic agent behavior. This paper focuses on the co-routing problem. [Copyright &y& Elsevier]

Abstract: Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely (i) create a motive to carpool, (ii) communicate this motive with other interested agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans and (v) provide a feedback to all concerned agents. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. In this paper we present a conceptual design of an agent-based model (ABM) for the carpooling application that serves as a proof of concept. Our agent-based model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our agent-based carpooling application we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm and a utility function to trigger the negotiation process between agents. We plan to, as a part of the future work, develop a prototype of our agent-based carpooling application on the basis of the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data. [Copyright &y& Elsevier]