Dynamic demand estimation for an AMoD system in Paris

Hörl, Sebastian; Balać, Miloš; Axhausen, Kay W.

doi:10.1109/ivs.2019.8814051

Cited by 29 publications

(21 citation statements)

References 29 publications

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“…We concentrate on the trips made in private vehicles and investigate the scenario in which all of these trips are served by on-demand vehicles instead. Investigating changes in mode choice due to availability of OV or AV as a travel option is an important question, but is beyond the scope of the current work as it has been studied extensively elsewhere 16,18,[35][36][37][38] . Our methodology could however be easily scaled and applied to cases with other assumptions of travel demand 28 .…”

Section: Resultsmentioning

confidence: 99%

Addressing the “minimum parking” problem for on-demand mobility

Kondor

Santi

et al. 2020

Sci Rep

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Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.

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Section: Resultsmentioning

confidence: 99%

Addressing the “minimum parking” problem for on-demand mobility

Kondor

Santi

et al. 2020

Sci Rep

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“…1) is similar to other, typically DTA-based transportation models. This includes on-demand mobility fleet models implemented in MATSim [16] or mobitopp [17] (shown on the left of this Figure). On-demand fleet operators need to query a very large number of calls to a routing engine based on the last network state to plan the movements of their fleets and generate realistic offers.…”

Section: A Workflow Of Transportation Modelsmentioning

confidence: 99%

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

Dandl¹,

Tilg²,

Rostami-Shahrbabaki³

et al. 2020

Preprint

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The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

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“…While the initial work, e.g., [6], [7], [8], [9] focuses only on ride-sharing, approaches in [10], [4], [3], [11], [12] combine rebalancing and ride sharing as their mutual impact has been recognized. Rebalancing in these is, however, done either at fixed intervals rather than dynamically [10], or using a centralized approach [4], [13], [12], [14]. A decentralized rebalancing approach is presented in [11], but in combination with a centralized request and ride-sharing assignment.…”

Section: Introductionmentioning

confidence: 99%

“…The only approaches that consider congestion with ridesharing and/or rebalancing are [9], [12], [13], [14]. However [15] Hörl et al [13] Zhang et al [6] Simonetto et al [7] Lu et al [8] Levin et al [9] Martinez et al [16] Fagnant and Kockelman [3] Fiedler [10] Alonso-Mora et al [4] Wen et al [11] Vosooghi et al [12] Ruch et al [14] SAMoD Guériau and Dusparic [5] This paper none uses a congestion-aware routing strategy to react to observed congestion level. [9], [12] only account for congestion generated by the fleet of SAVs themselves rather than other vehicles, and in [14], [13] the agent-based simulation used relies on a simplified queue-based model for traffic congestion instead of more realistic car-following behaviours.…”

Section: Introductionmentioning

confidence: 99%

“…However [15] Hörl et al [13] Zhang et al [6] Simonetto et al [7] Lu et al [8] Levin et al [9] Martinez et al [16] Fagnant and Kockelman [3] Fiedler [10] Alonso-Mora et al [4] Wen et al [11] Vosooghi et al [12] Ruch et al [14] SAMoD Guériau and Dusparic [5] This paper none uses a congestion-aware routing strategy to react to observed congestion level. [9], [12] only account for congestion generated by the fleet of SAVs themselves rather than other vehicles, and in [14], [13] the agent-based simulation used relies on a simplified queue-based model for traffic congestion instead of more realistic car-following behaviours. In other approaches, simplifications were used to mimic the behaviour in the presence of congestion, e.g., modifying the speed of SAVs per simulated period: one speed at rush hour, and another during the quiet period [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Shared Autonomous Mobility on Demand: A Learning-Based Approach and Its Performance in the Presence of Traffic Congestion

Guériau

Cugurullo

Acheampong

et al. 2020

IEEE Intell. Transport. Syst. Mag.

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Mobility-on-demand systems consisting of shared autonomous vehicles (SAVs) are expected to improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, several issues in their implementation remain open, such as unifying the vehicle and ride-sharing assignment with rebalancing non-occupied vehicles. Furthermore, proposed SAV systems are evaluated in isolation from other traffic; no congestion is taken into account when assigning requests or calculating routes. To address this gap, we present Shared Autonomous Mobility-on-Demand system (SAMoD), a reinforcement learning-based approach to vehicle relocation and ride-sharing request assignment. Each vehicle learns its pickup and rebalancing behaviour based on local current and observed historical demand. We evaluate SAMoD on Manhattan network using NYC taxi data in microsimulator SUMO. We investigate SAMoD performance in the presence of congestion generated by private vehicles, as well as investigate impact of different percentages of SAMoD vehicles in the system on overall traffic network performance.

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Dynamic demand estimation for an AMoD system in Paris

Cited by 29 publications

References 29 publications

Addressing the “minimum parking” problem for on-demand mobility

Addressing the “minimum parking” problem for on-demand mobility

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

Shared Autonomous Mobility on Demand: A Learning-Based Approach and Its Performance in the Presence of Traffic Congestion

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