Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH

Vyas, Gaurav; Famili, Pooneh; Vovsha, Peter; Fay, Daniel; Kulshrestha, Ashish; Giaimo, Greg; Anderson, Rebekah

doi:10.1007/s11116-019-10030-w

Cited by 19 publications

(24 citation statements)

References 13 publications

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“…Micromobility refers to a variety of small transport modes operating at low speeds, typically below 25 km/h, such as bicycles, electric bicycles, or X Hebenstreit and Martin [35] X Heilig et al [36] X Heilig et al [37] X X Hörl et al [38] X Lavieri et al [39] X X Levin and Boyles [22] X Liu et al [40] X Martínez et al [42] X X Martínez et al [42] X Millard-Ball [43] X Nahmias-Biran et al [23] X X Oh et al [20] X X Rodier et al [44] X Truong et al [45] X X Vyas et al [46] X Wadud et al [47] X Wang, Winter and Tomko [48] X Wang et al [49] X Wen et al [50] X X Yin et al [51] X Zhang et al [52] X Zhang et al [53] X scooters [67]. In this paper, the authors focus on novel shared micromobility systems and their impact on everyday mobility choices.…”

Section: Micromobility Sharing Systemsmentioning

confidence: 99%

“…Upon the review of numerous articles that focused on the subject, the authors have classified those changes to be the following: (i) acceptance of longer trips, (ii) change in daily activity timing, (iii) increased number of non-mandatory trips, (iv) increased number of trips of mobility impaired, (v) modal change, (vi) relocation, (vii) shifts in parking habits, and (viii) shifts in vehicle ownership. None of the reviewed studies has considered all of the identified behavioural changes, with study by Vyas et al [46] omitting just the relocation aspect, and study by Childress et al considering 5 behavioural shifts [31]. Moreover, the most frequently considered behavioural shift was a modal change with 31 studies incorporating it, followed by the acceptance of longer trips (12 studies), changes in daily activity timing (7 studies), shifts in parking habits (5 studies), increased number of non-mandatory trips (4 studies), increased number of trips of mobility impaired (4 studies), shifts in vehicle ownership (4 studies), and relocation (2 studies).…”

Section: Nms Impact On Travel Behaviourmentioning

confidence: 99%

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Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models

Garus

Alonso

Raposo

et al. 2022

Journal of Advanced Transportation

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Advancement in the fields of electrification, automation, and digitalisation and emerging social trends are fuelling the transformation of road transport resulting in the introduction of various innovative mobility solutions. Yet the reaction of people to many of the new solutions is still vastly unknown. This creates an unprecedented quandary for transport planners who are requested to design future transport systems and create the related investment plans without fully validated models to base the assessment upon. As some evidence on citizens’ behaviour concerning new mobility solutions starts to be progressively made available, first attempts to update the existing models begin to emerge. Nevertheless, a lot more is needed as some of the transpiring mobility solutions have not yet reached the market, making the corresponding behaviour changes imponderable. In this context, the main purpose of this paper is to provide a review on how travel behaviour changes linked to the deployment of new mobility solutions have been considered in travel demand models. The new mobility solutions studied include carsharing, dynamic ridesharing, micromobility sharing services, and personal and shared autonomous vehicles. An overview and comparison of relevant studies implementing activity or trip-based demand models and other methodologies are presented. The analysis shows that the results of the different studies heavily depend on the extent to which behavioural changes are considered. The results of the review thus point to the need for holistic demand models that carefully mimic the urban reality with everything it has to offer and account for the importance of individual traits in the decision-making processes. Such models need an in-depth understanding of the microscopic mechanisms leading to the travel behaviour shifts linked to the most innovative mobility solutions. To achieve this level of detail, mobility living labs and their real-life experiments and experience with citizens, which are flourishing in Europe, are suggested to play a crucial role in the years to come.

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Section: Micromobility Sharing Systemsmentioning

confidence: 99%

Section: Nms Impact On Travel Behaviourmentioning

confidence: 99%

Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models

Garus

Alonso

Raposo

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…(3): Each destination is serviced by only one vehicle and one origin. (5,8,9): Starts of service times are feasible. (6, 10): Vehicle occupancy does not exceed vehicle capacity.…”

Section: The Dynamic Dial-a-ride Problemmentioning

confidence: 99%

“…As a consequence, it is not surprising to see research on the uptake of this new transport mode in urban areas ( 3 – 6 ), using a behavioral modeling framework similar to those used in modern forecasting models (e.g., discrete choice modeling). At the same time, we have already started to see the incorporation of some anticipated characteristics of CAVs into forecasting models ( 7 , 8 ), and it is to be expected that some of these characteristics and model structures will be leveraged to model TNCs.…”

mentioning

confidence: 99%

Mobility-as-a-Service and Demand-Responsive Transport: Practical Implementation in Traditional Forecasting Models

Camargo¹,

Pammenter

Inayathusein³

2020

Transportation Research Record

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The increasing effect of Transport Network Companies (TNCs) in major US cities, allied to the expectation that connected and autonomous vehicles (CAVs) will become the prevailing type of automobile on the streets in the coming decades, requires such a trend to be reflected in our forecasting models. Yet, most of the efforts undertaken in the United States and elsewhere are largely focused on understanding the demand for such types of transportation and have left aside the analysis of crucial differences in envisaged systems, such as pooled versus single-occupancy vehicles, use of CAVs as access modes to mass transit, and the cost corresponding to different levels of service in citywide systems. In this paper we introduce a new algorithm for modeling pooled CAVs and a framework for integrating this into traditional forecasting models. We also present the preliminary results of an application of the proposed methodology to the metropolitan region of Vancouver, British Columbia. The results are promising, although a few implementation choices made for this study have resulted in poor computational performance.

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“…The two methods have been used to explore long-term changes in travel-related behavior such as residential and work location choices and short-term changes such as daily activity patterns. For instance, simulation studies consistently find that the introduction of (shared) AVs will lead to an increase in vehicle miles traveled (VMT) (e.g., Childress et al [ 4 ]; Taiebat et al [ 5 ]), the number of vehicle trips (e.g., Bernardin et al [ 6 ]; Vyas et al [ 7 ]), and the average trip length (e.g., Auld et al [ 8 ]; Thakur et al [ 9 ]). Moreover, the literature indicates that AV options are likely to cannibalize transit ridership (e.g., Kröger et al [ 10 ], World Economic Forum [ 11 ]), largely owing to the assumption made on the reduction of AV riders’ value of time, which are backed by findings from survey studies (e.g., Malokin et al [ 12 ]; Zhong et al [ 13 ]).…”

mentioning

confidence: 99%

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

Harb

Malik

Circella

et al. 2021

Transportation Research Record

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To explore potential travel behavior shifts induced by personally owned, fully autonomous vehicles (AVs), we ran an experiment that provided personal chauffeurs to 43 households in the Sacramento region to simulate life with an AV. Like an advanced AV, the chauffeurs took over driving duties. Households were recruited from the 2018 Sacramento household travel survey sample. Sampling was stratified by weekly vehicle miles traveled (VMT), and households were selected to be diverse by demographics, modal preferences, mobility barriers, and residential location. Thirty-four households received 60 h of chauffeur service for 1 week, and nine households received 60 h per week for 2 weeks. Smartphone-based travel diaries were recorded for the chauffeur week(s), 1 week before, and 1 week after. During the chauffeur week, the overall systemwide VMT (summing across all sampled households) increased by 60%, over half of which came from “zero-occupancy vehicle” (ZOV) trips (when the chauffeur was the only occupant). The number of trips made in the system increased by 25%, with ZOV trips accounting for 85% of these additional trips. There was a shift away from transit, ridehailing, biking, and walking trips, which dropped by 70%, 55%, 38%, and 10%, respectively. Households with mobility barriers and those with less auto dependency had the greatest percent increase in VMT, whereas higher VMT households and families with children had the lowest. The results highlight how AVs can enhance mobility, but also caution against the potential detrimental effects on the transportation system and the need to regulate AVs and ZOVs.

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Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH

Cited by 19 publications

References 13 publications

Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models

Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models

Mobility-as-a-Service and Demand-Responsive Transport: Practical Implementation in Traditional Forecasting Models

Glimpse of the Future: Simulating Life with Personally Owned Autonomous Vehicles and Their Implications on Travel Behaviors

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