Aggregated Modeling of Urban Taxi Services

Grau, Josep Maria Salanova; Estrada, Miquel; Amat, Carles

doi:10.1016/j.sbspro.2014.12.147

Cited by 28 publications

(11 citation statements)

References 18 publications

(16 reference statements)

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“…Nevertheless, there are significant works analyzing MAAS and its possible implications, studies intended to quantify the level of satisfaction perceived by taxi users, and a strong body of literature regarding how users choose their transportation mode [19][20][21]. In addition to this, there is research studying the operative issues of taxis businesses [22,23]. A preliminary literature review suggests that the main data source for this kind of analysis would be composed by stated preferences surveys where people is asked to choose a transport mode to commute considering different characteristics of the modes; furthermore, they are asked about their preferences when the characteristics of the modes vary.…”

Section: Tncs and Travel Behaviormentioning

confidence: 99%

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

2020

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This paper proposes a modal-shift analysis methodology based on a mix of small-scale primary data and big data sources to estimate the total amount of trips that are reallocated to transportation network companies (TNCs) services in Bogotá, Colombia. The analysis is focused on the following four modes: public transportation, private vehicles, conventional taxis, and TNC services. Based on a stated preferences survey and secondary databases of travel times and costs, the paper proposes a methodology to estimate the reallocation of travel demand once TNCs start operating. Results suggests that approximately one third of public transportation trips are potentially transferred to TNCs. Moreover, potential taxi and private vehicle–transferred trips account for almost 30% of the new TNC demand. Additionally, approximately half of the trips that are reallocated from public transport demand can be considered as complementary, while the remaining share can be considered as potential replacing trips of public transportation. The paper also estimates the potential increase in Vehicle-km travelled in each of the modes before and after substitution as a proxy to the effects of demand reallocation on sustainability, finding increases between 1.3 and 14.5 times the number of Vehicle-km depending on the mode. The paper highlights the role of open data and critical perspectives on available information to analyze potential scenarios of the introduction of disruptive technologies and their spatial, social, and economic implications.

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Section: Tncs and Travel Behaviormentioning

confidence: 99%

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

2020

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“…All costs are estimated using continuous approximations and geometric probabilities while the optimization is done analytically. Salanova et al (2014) review various formulations developed for the estimation of the variable of the taxi market, some of them used in the model. The model is presented in detail in Salanova (2013) and is complemented with the following three additions: elastic demand; 24h demand profile and shifts policy.…”

Section: Modeling For Taxi Policymentioning

confidence: 99%

Social optimal shifts and fares of taxi services

Grau

Estrada

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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SUMMARYThis paper presents a mathematical model for supporting the decision and policy making related to the optimal determination of shifts and fares of taxi services in a major city. The model is based on the system's generalized cost function and uses continuous approximations and geometric probabilities for estimating the key performance indicators of the taxi market, which are waiting and access time for the customers (in-vehicle travel time does not depend on the offer side) and benefits for the drivers. The model is based on an econometric model with the inclusion of an elastic demand, which allows the estimation of the optimal values for the two decision variables of the problem: fare structure and the taxi fleet size. The model also accounts for a full-day time period instead of the one-hour time frame models used in most of the taxi models, which allows for providing insights on the daily duration and distribution of the shifts that should be defined by the policy makers.

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“…Salanova et al . reviewed formulations to estimate various metrics such as fleet size, generalized cost and optimum fleet of taxi services in urban regions. The variation of taxi fleet size and their passenger demand can influence the customer‐search behavior of vacant taxis.…”

Section: Related Literaturementioning

confidence: 99%

“…Salanova et al [7] reviewed models developed for taxicab problems from a market organization, operational organization and regulation issues perspectives. Salanova et al [8] reviewed formulations to estimate various metrics such as fleet size, generalized cost and optimum fleet of taxi services in urban regions. The variation of taxi fleet size and their passenger demand can influence the customer-search behavior of vacant taxis.…”

Section: Related Literaturementioning

confidence: 99%

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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Summary Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.

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Aggregated Modeling of Urban Taxi Services

Cited by 28 publications

References 18 publications

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá

Social optimal shifts and fares of taxi services

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

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