Automated Mobility-on-Demand vs. Mass Transit: A Multi-Modal Activity-Driven Agent-Based Simulation Approach

Basu, Rounaq; Araldo, Andrea; Akkinepally, Arun; Nahmias-Biran, Bat-hen; Basak, Kalaki; Seshadri, Ravi; Deshmukh, Neeraj; Kumar, Nishant; Azevedo, Carlos Lima; Ben‐Akiva, Moshe

doi:10.1177/0361198118758630

Cited by 106 publications

(92 citation statements)

References 18 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%

“…Due to the nature of the modeling, this process results in one day's data, that can be considered as a typical workday in Singapore. We are not using SimMobility's capabilities to evaluate changes in mode share due the introduction of ride-hailing and AVs 35,37 ; instead, we are assuming that every trip made in private vehicles today would be substituted with a ride in an OV, thus we can investigate what are the implications of such a setup under simplified conditions.…”

Section: Methodsmentioning

confidence: 99%

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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%

Section: Methodsmentioning

confidence: 99%

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

Kondor

Santi

et al. 2020

Sci Rep

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“…The Long-Term (LT) component involves creation of a synthetic population, followed by household-level residential location and vehicle availability choices, and individual-level job or school location choices, at the temporal scale of days to years (Zhu et al, 2018). The Medium-Term (MT) component couples a mesoscopic supply simulator with a microscopic demand simulator that involves mode choice, route choice, and activity-travel pattern generation at the temporal scale of minutes up to a day (Basu and et al, 2018). The LT and MT components are connected through individual-specific ABA measures that are disaggregate utility-based measures of alternative daily activity patterns (logsums) generated by MT, which are then used as explanatory variables in LT choice models.…”

Section: Simmobilitymentioning

confidence: 99%

Can increased accessibility from emerging mobility services create a car-lite future? Evidence from Singapore using LUTI microsimulation

Basu

Ferreira

2020

Transportation Letters

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Emerging mobility services are being increasingly regulated, with several cities testing policy interventions in pilot study areas before a metro-wide implementation. This is motivated by the expectation that increased accessibility from these services might induce households to relinquish private vehicles, and demonstrate the viability of car-lite strategies that could be expanded to metro-wide areas. However well-intended, the increased attractiveness of such test areas might also increase neighborhood attractiveness in ways that cause gentrification. This study uses an agent-based LUTI microsimulation model to examine the effect of accessibility improvements on private mobility holdings, both directly and through the mediating effect of housing choice. A scenario-based design is adopted, wherein different types of market responses to a pilot car-lite policy are modeled through various assumptions of changes in model parameters. Three study areas with different characteristics are chosen to explore the spatial variation in policy effects. Our findings indicate that in-movers are more likely to be higher-income households that have a comparatively higher car ownership rate. Such unintended negative consequences like gentrification can significantly undermine the potential boosts to vehicle-free behavior inside the study area. As a possible step to mitigate such consequences, we suggest that emerging mobility regulation strategies anticipate and address the likely response of the housing market and urban redevelopment opportunities.

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“…SimMobility is unique in that the same pool of agents is used across all timeframes: agents' long-term behavior is already established when their behavior is modelled in the midterm/short-term simulation. SimMobility has been applied to explore how future scenarios induce shifts in the distribution of people, activities, land use, and transportation network performance in several contexts: autonomous mobility-on-demand (32,33), freight (34), public transit (35,36), pricing (37). This paper focuses on the demand calibration of SimMobility Short-term (ST) which simulates the high-resolution movement of agents (traffic, transit, pedestrians and goods) and the operation of different mobility services and control systems.…”

Section: Multimodal Microscopic Traffic Simulationmentioning

confidence: 99%

Demand Calibration of Multimodal Microscopic Traffic Simulation using Weighted Discrete SPSA

Seshadri

Azevedo

et al. 2019

Transportation Research Record

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This paper presents a stochastic approximation framework to solve a generalized problem of off-line calibration of demand for a multimodal microscopic (or mesoscopic) network simulation using aggregated sensor data. A key feature of this problem is that demand, although typically treated as a continuous variable is in fact discrete, particularly in the context of agent-based simulation. To address this, we first use a discrete version of the weighted simultaneous perturbation stochastic approximation (W-DSPSA) algorithm for minimizing a generalized least squares (GLS) objective (that measures the distance between simulated and observed measurements), defined over discrete sets. The algorithm computes the gradient at each iteration using a symmetric discrete perturbation of the calibration parameters and a multimodal weight matrix to improve the accuracy of the gradient estimate. The W-DSPSA algorithm is then applied to the large-scale calibration of multimodal origin–destination (OD) flows (including private vehicle (PVT) and public transit (PT) trips) in a microscopic network simulation model of Singapore. The results indicate that an acceptable margin of error on the vehicle loop count (VLC) and bus passenger count (BPC) are achieved at convergence with an improvement of 60%~80% in root mean squared errors. Lastly, we validate the calibration results with observed travel times on the network. Statistical comparison shows good agreements on both point-to-point travel time (PTT) and public buses’ stop-to-stop ride-time (SRT) with the field observations.

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Automated Mobility-on-Demand vs. Mass Transit: A Multi-Modal Activity-Driven Agent-Based Simulation Approach

Cited by 106 publications

References 18 publications

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

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

Can increased accessibility from emerging mobility services create a car-lite future? Evidence from Singapore using LUTI microsimulation

Demand Calibration of Multimodal Microscopic Traffic Simulation using Weighted Discrete SPSA

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