Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach

Nahmias-Biran, Bat-hen; Oke, Jimi; Kumar, Nishant; Azevedo, Carlos Lima; Ben‐Akiva, Moshe

doi:10.1007/s11116-020-10106-y

Cited by 34 publications

(19 citation statements)

References 28 publications

(31 reference statements)

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“…This allows modeling and forecasting travel behavior that is sensitive to socioeconomic features and individual characteristics, to a level that is not achievable using the traditional four-step models. Moreover, the agents make travel demand choices based on the transport supply performance, which allows, for example, to implicitly generate induced demand [7]. These features were relevant already before the COVID-19 crisis and it is fair to assume that the pandemic will speed up changes in mobility behavior.…”

Section: Introduction 1context and Motivationmentioning

confidence: 99%

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

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Section: Introduction 1context and Motivationmentioning

confidence: 99%

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

Self Cite

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“…SAV real-time requests are submitted to the system shortly before departure and need to be arranged as soon as possible. erefore, SAV real-time request systems mostly use agentbased or activity-based models [12][13][14][15][16][17] to match vehicles and requests to better research the dispatching of SAVs from a micro perspective. SimMobility, Transims, MATSim, or other simulation platforms are typically used.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System

Yang

Cui

et al. 2022

Journal of Advanced Transportation

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With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing.

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“…This approach in MATSim was improved by Ho¨rl et al with a more advanced fleet control and a mode-choice model after iterations of daily network simulations (35). A similar approach is available in SimMobility, which models a transportation system in three timescales (36). Activity planning and mode choice-including the choice to use MoD systems-are determined pre-day and the MoD system is simulated in a stage called supply.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

Wilkes

Engelhardt

Briem

et al. 2021

Transportation Research Record

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This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach

Cited by 34 publications

References 28 publications

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

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