Vehicle scheduling for on-demand vehicle fleets in macroscopic travel demand models

Hartleb, Johann; Friedrich, Markus; Richter, Emely

doi:10.1007/s11116-021-10205-4

Cited by 11 publications

(14 citation statements)

References 29 publications

(34 reference statements)

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“…Up-to-date information on actual demand is received by the system every 15 min and updates the system's functioning. Another research project used the adjusted four-step algorithm to solve the vehicle scheduling problem, aiming to minimize the number of serving vehicles for the on-demand transport services [16]. The authors propose to incorporate on-demand services into the macroscopic travel demand model, expanding the model by adding supplementary steps to the conventional four.…”

Section: Literature Reviewmentioning

confidence: 99%

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Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

2022

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Simulation models of transport systems are a key tool for solving many problems in the field of management of these systems. The methodologies for creating such models use datasets on both transport infrastructure and demand for the delivery of goods or passenger transport, however, many factors are considered based on assumptions due to the complexity. This article describes the approach to modeling the cargo transportation system for road transport in Poland based on data obtained by the Central Statistical Office from the TD-E survey. This approach avoids many assumptions about demand as the demand parameters are estimated based on a sample representing the general population—a set of all economic entities generating freight traffic. Basic procedures in the developed approach have been implemented as Python scripts. As a result of the use of the proposed methodology, a country-wide road transport model was obtained based on the TD-E survey from 2018. The adequacy of the developed model was assessed based on the results of the General Traffic Measurement from 2015. The obtained model is of satisfactory quality (the coefficient of determination equals 0.62), which can be improved after calibrating the space resistance functions and improving the traffic distribution procedure.

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Section: Literature Reviewmentioning

confidence: 99%

“…ParameterValue Share of values below 10 veh./day (%)16.52 Share of values from 10 to 50 veh./day (%)23.64…”

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confidence: 99%

Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

2022

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“…Fagnant and Kockelman (2018) extended agent-and network-based simulations to include dynamic ride-sharing in shared AVs and studied its role in reducing average service time and travel costs. Hartleb et al (2021) proposed efficient heuristics for a fleetsizing problem that serves demand estimated from a macroscopic model. Grahn et al (2021) proposed a heuristic-based approach for demand matching and vehicle routing to cater to on-demand FLM requests.…”

Section: Literature Reviewmentioning

confidence: 99%

An agent-based fleet management model for first- and last-mile services

2022

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With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first-and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent-and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

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“…This approach has been successfully implemented for regional modeling studies in the context of automated driving ( 9 , 10 ). Applying the scheduling algorithm by Hartleb et al ( 11 ), it is possible to determine relocation trips and fleet sizes.…”

Section: Introductionmentioning

confidence: 99%

“…Since the suppliers’ tours in this case solely depend on the demanders’ trips, the demander paths are matched with themselves following the previously described steps. Finally, fleet size and relocation trips of the ride-sharing vehicles are determined with the scheduling method described by Hartleb et al ( 11 ) and Richter et al ( 10 ). It is a heuristic approach at optimizing vehicle usage.…”

Section: Introductionmentioning

confidence: 99%

Implementing a Ride-sharing Algorithm in the German National Transport Model (DEMO)

Thomsen

2022

Transportation Research Record: Journal of the Transportation R

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As macroscopic travel demand models are widespread among regional modelers, it is crucial to develop extensions for new mobility services such as ride-sharing services to assess their effects on the transport system. In a feasible approach that was introduced with previous research, origin-destination network-based paths are transformed to a series of crossed traffic analysis zones (TAZs). These series are compared to find overlapping trajectories to derive ride-sharing potential. However, in national transport models the TAZ sizes are larger than in regional models, which can lead to unrealistically high matching rates because of imprecise path trajectories. This paper presents a modified approach for implementation in the German national transport model, DEMO. The main adjustment is to use population centers as pick-up locations instead of TAZs. Each TAZ includes multiple points. This way, the trajectories of paths can be better differentiated. Another advantage of this method is that intrazonal trips can be distributed between the centers and thus be included in the algorithm. With the successful implementation in DEMO, the effects of new mobility tools in German cities can be evaluated within the same model.

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Vehicle scheduling for on-demand vehicle fleets in macroscopic travel demand models

Cited by 11 publications

References 29 publications

Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

An agent-based fleet management model for first- and last-mile services

Implementing a Ride-sharing Algorithm in the German National Transport Model (DEMO)

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