Taxi Rides Research Articles

Bayesian multivariate spatiotemporal statistical modeling of bus and taxi ridership

Statistical modeling of ridership over both space and time provides valuable insights on transportation planning and policies. Existing spatiotemporal studies, however, predominantly focus on analyzing a single type rather than multiple types of ridership, thus cannot leverage the correlation between different types of ridership. This study proposes a Bayesian multivariate spatiotemporal statistical model to jointly analyze multiple ridership over time. Specifically, the model accounts for correlation between multiple ridership based on different assumptions of space-time interactions (i.e., departures from the main spatial and temporal patterns) between different types of ridership as well as if covariates are included in the model. Using hourly bus and taxi ridership in the city of Wuhu, China as an example, the case study indicates that accounting for the correlation between the space-time interactions of each ridership, beyond the correlation between the main spatial patterns of the two ridership, further improves the statistical inferences of ridership modeling. In addition, the proposed approach enables the detection of spatial and spatiotemporal hotspots of each ridership as well as bus-taxi ratio hotspots using posterior probabilities. It also supports visual inspections regarding how the inclusion of covariates explains these hotspots. The proposed approach not only advances multivariate spatiotemporal statistical modeling of ridership, but can also provide useful insights on space- and time-specific transport policies at a granular resolution.

Prediction of Taxi Ridership in New York City based on ARIMA Model

Prediction of Taxi Ridership in New York City based on ARIMA Model

Strategic integration of vertiport planning in multimodal transportation for urban air mobility: A case study in Beijing, China

In pursuit of strategic sustainable development goals, this study seeks to analyze the spatial placement of vertiports for urban air mobility, positioning them as integral components within the multimodal transportation system. We propose an iterative weighted k-means clustering method that incorporates the value of travel time of different transportation modes to identify potential vertiport locations. A multi-objective optimization model that maximizes total air taxi ridership and minimizes total facility costs and travel distances to vertiports is developed considering the constraints of the distance from vertiports to existing mobility hubs. A case study is conducted using the data of Beijing city, incorporating the modes of taxis and metro. We found that 19 vertiports can guarantee a robust performance of urban air mobility service. Users of urban air mobility can benefit from substantial travel time savings of 80% (48 min on average). This study contributes to a deeper understanding of the optimal setting of urban air mobility facilities from the perspective of network synchronization, offering useful insights into policy decision-making for sustainable infrastructure planning.

Focal points for giving

Leveraging the unique context of New York City taxi rides, we estimate that passengers are more likely to tip a suggested amount when presented with an integer tip suggestion, which also corresponds with an increase in average tip rates. We show that these results are not driven by rounding heuristics and are consistent with integers acting as focal points instead of left-digit bias. Focusing on a 2012 rate fare change that increased the probability of integer tip suggestions by 700%, our estimates imply a transfer from riders to drivers of approximately 250,000 dollars in 2013.

When Bankers Go to Hail: Insights into Fed–Bank Interactions from Taxi Data

We introduce taxi ridership between the Federal Reserve (Fed) Bank of New York and large financial institutions headquartered in New York City as a novel proxy for Fed–bank face-to-face interactions. We document a negative relation between past Fed–bank interactions and future stock market returns, particularly on days around the Fed’s public announcements. We also find significantly elevated Fed–bank interactions immediately following the lifting of the Federal Open Market Committee blackout. Our findings suggest that the Fed increases its information gathering via face-to-face interactions when it possesses negative private information about the condition of the economy. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Muma College of Business Center for Analytics and Creativity at the University South Florida, the George Stigler Center for the Study of the Economy and the State, the Lynde and Harry Bradley Foundation, the University of Chicago Booth School of Business, the Liew Fama-Miller Fellowship, and the Fischer Black Fellowship. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4885 .

A multiscale spatial analysis of taxi ridership

Taxi plays a supplement role in sustainable development of urban public transport systems. However, the extent to which the built environment affects taxi ridership at various spatial scales deserves further exploration because understanding the true spatial heterogeneity across a varying scale could be valuable for both global and localized policy decision-makings. In this study, we attempt to analyze and discuss the spatial predictors of taxi ridership by utilizing an multiscale geographically weighted regression (MGWR) model and comparing the model's performance to that of ordinary least square (OLS) and geographically weighted regression (GWR) models. Using the taxi data of Xi'an city, we found that the MGWR model could explain 81.8% of the total taxi ridership fluctuations and allows localized and targeted policy makings to help taxi drivers search for passengers and to improve passengers' taxi-hailing experiences in specific districts.

Exploring the association between travel demand changes and the built environment during the COVID-19 pandemic

This study compared the extent to which COVID-19 impacted travel demand of bike-sharing and taxi in New York City, and further explored how the changes in travel demand were associated with the built environment through four typical regression models, namely, least squares (OLS) regression, geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models. In particular, this study looked at two phases: the lockdown phase (during which travel demand decreased dramatically) and initial recovery phase (during which travel demand started to increase). The results suggested that 1) GTWR performed better than the other three model types; 2) shared bike ridership rebounded much more significantly during the recovery phase than taxi ridership; 3) Commercial Point of Interest (POI) was positively associated with the change of ridership in both lockdown and recovery phases.

Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression

Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression

Going the Extra Mile: What Taxi Rides Tell Us About the Long-Hour Culture in Finance

We analyze banks’ “protected-weekend” policies that restrict junior bankers from working during weekends. We use taxi rides from bank addresses in New York City to infer bankers’ working hours. We find the policies induced bankers to shift their work to late-night hours on weekdays. We then investigate whether such shifts in working hours affected the quality of work. After the policy, analysts of the policy-implementing banks make more errors in their earnings forecasts. They also herd more toward the consensus in their forecasts. We further provide evidence that junior bankers are the most adversely affected by the policy. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4774 .

An On-Demand Dynamic Taxi Crowdshipping Model for Urban Parcel Delivery

Abstract In this article, a dynamic crowdshipping model is presented to use the vacant space of taxis to deliver parcels while serving passengers. A mixed integer linear programming (MILP) model within a rolling horizon framework that periodically updates input information is formulated. The objective function aims to maximize the number of matched trips while controlling the total travel time in the system. The developed model is tested using the taxi ridership and the household travel survey data in New York City, ensuring the credibility of the proposed model in addressing the goals of the study and its potential application to mitigate transportation externalities.

Analysis of mobility patterns for urban taxi ridership: the role of the built environment

Analysis of mobility patterns for urban taxi ridership: the role of the built environment

Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access

Witnessing the accelerated commercialization efforts for air taxi services in across metropolitan cities, our research focuses on infrastructure planning of skyports. We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in a city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.

The feasibility of platform cooperatives in the gig economy

In view of the precarity and economic dependency of gig workers, platform cooperatives come into the picture as alternatives to investor-owned platforms. We develop a taxonomy of platform cooperatives along the dimensions of ownership of the platform and employment by the cooperative. Platform cooperatives are then examined as worker-run matchmaking platforms for gigs, by analysing their challenges, highlighting the difficulties to raise capital, take collective decisions, and gain institutional support. On the basis of a feasibility analysis, we conclude that the identified challenges can most likely be successfully overcome by platform co-ops that organise taxi rides and professional jobs, while it may prove much more difficult in food delivery, homecare and micro-tasking.

Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data

Taxis are an important component of the urban public transportation system, with wide geographical coverage and on-demand services characteristics. Thorough understanding of the built environment affecting taxi ridership can enable transportation authorities to develop targeted policies for transportation planning. Previous studies in this field had few data sources and did not consider the spatiotemporal variability. This study aims to develop an analytical framework for understanding the spatiotemporal correlation between the urban built environment and taxi ridership, which is empirically analyzed in New York City. The built environment is defined through multisource data in terms of density, design, diversity, and destination accessibility. Besides the exploration of travel patterns, the spatiotemporal heterogeneity of taxi ridership is modeled using geographically and temporally weighted regression (GTWR). The result shows that GTWR outperforms ordinary least squares (OLS), geographically weighted regression (GWR), and temporally weighted regression (TWR) in both goodness of fit and explanatory accuracy. More importantly, our study found that land use diversity is negatively correlated with taxi ridership, while transportation diversity is positively correlated with it. A highly accessible road network improves the people’s demand for taxis in the morning rush hours. Moreover, the density of railway stations is positively correlated with taxi ridership on weekdays but adversely on weekends. These findings provide practical insights for urban transportation policy development and taxicab regulation.

Jointly modeling bus and taxi ridership: A Bayesian multivariate spatial analysis accounting for cross‐ridership correlation

AbstractUnderstanding the spatial patterns of transit ridership and their ascribable factors is critical for urban and transportation planning. Existing studies predominantly examine a single type of transit ridership but neglect important cross‐ridership correlation. This study aims to develop a Bayesian multivariate spatial modeling approach for jointly analyzing multiple ridership. In particular, cross‐ridership correlation is estimated using correlated spatially unstructured residuals, correlated spatial random effects, or the combination of both. The proposed approach is applied to a case study of Wuhu, China, where the correlation between bus and taxi ridership is accounted for in the modeling of transit ridership and their built environment determinants. Results show that accounting for cross‐ridership correlation significantly improves model‐fitting, but the fashion used to induce such a correlation matters. Furthermore, the role of built environment in determining transit ridership could be over‐estimated without considering cross‐ridership correlation. This study contributes to statistical modeling of transit ridership by applying a spatial multivariate approach, which can improve our understanding of the underlying processes of multiple ridership, their interactions, as well as their associations with physical environments.

Inferring land use from spatialtemporal taxi ride data

Different functions of urban spaces generate travel demand, and urban travel inversely influences urban spaces. Collective human mobility patterns can reveal land use patterns and urban structure although previous studies constrained the area around transportation stations. This paper explores the relationship between urban movements and land use patterns of a whole city using taxi ridership data in Seoul, South Korea. This study takes advantages of taxi datasets that are not bounded by routes and stations and contain more precise information in terms of space and time. In addition, we verify the connection between land use and travel patterns using a statistical method. To reveal the interaction between trip mobility patterns and land use, taxi ridership patterns are grouped by a clustering method. Each clustered pattern is then analyzed with the corresponding urban context. We also statistically verify the relationship between land use and the pattern clusters, and show that the seven obtained clusters can be categorized into four types and were significantly correlated with land use. Furthermore, more detailed land use can be classified using the spatiotemporal properties of taxi ridership patterns. The proposed method is expected to monitor change in land use development.

Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

New stations (such as metro stations) will bring remarkable changes to the local transportation and economic development. Understanding patterns of factors which importantly impact on public transit ridership in the surrounding areas of new stations is essential to their construction planning, like estimating the possible ridership. Built environment variables with high importance magnitude, which were thought applicable to estimate public transit ridership in other areas of the same category, were described as transferable variables (TVs) in this study. A transferability analysis method of the built environment for the ridership estimation was constructed by adopting partial least square regression (PLSR) based on available data. Taking Wuhan, China as an example, this study analyzed the changes and differences of the built environment variables in different categories of pedestrian catchment areas (PCAs) of metro stations on the importance and transferability magnitude for the metro and taxi ridership, based on the metro and taxi data of one week in January, April, and June. Performances of the ridership estimation based on TVs and all the built environment variables were compared. This study inferred that (1) most of the land use variables (about 85%) showed important influence on the metro and taxi ridership, while only about 18% of the other variables showed key impact. The importance magnitude of the built environment variables was mainly related to PCA categories and public transportation modes, but less related to time. (2) Highly important built environment variables also tended to be highly transferable. Transferability magnitude of the built environment variables for the ridership was related to PCA categories and types of public transport. (3) Compared to all the built environment variables, using TVs, the relative accuracy of the metro and taxi ridership estimation was around 20% and 18% higher respectively.

Urban Dwellings, Haitian Citizenships: Housing, Memory, and Daily Life in Haiti by Vincent Joos

Reviewed by: Urban Dwellings, Haitian Citizenships: Housing, Memory, and Daily Life in Haiti by Vincent Joos Chelsey L. Kivland Urban Dwellings, Haitian Citizenships: Housing, Memory, and Daily Life in Haiti. By Vincent Joos. New Brunswick, NJ: Rutgers University Press, 2002. ISBN: 9781501746994. 240 pp. $34.95 paperback. Of the City: Being and Becoming in the Fragility of Port-au-Prince Today In Urban Dwellings, Haitian Citizenships: Housing, Memory, and Daily Life in Haiti, Vincent Joos, assistant professor of French at Florida State University, accompanies several Port-au-Prince residents as they attempt to remake (and renew) their lives in the wake of the 2010 earthquake. Whether longstanding residents or post-disaster arrivals helping in the recovery, the contributors to this book want the country to develop its economy from the creativity, intelligence, and strong work ethic of the Haitian people. At times this vision can feel a bit aspirational, but then we are shown how entrepreneurs have repurposed their pasts to create a new business or organization post-earthquake. Through interviews, informal conversation, and everyday participant-observations, where he joins the activities of his interlocutors, Joos follows their pursuits of incomes and identities as the proprietors of “funeral parlors, beauty salons, restaurants, garages, and warehouses” (142). He takes us on walks, bus routes, and taxi rides, and to the front porches of urban houses and apartments. We learn that these are spaces where the talk of gwo politik and ti politik (“big politics” and “small politics,” meaning world-historical events and everyday affairs), and their interlinkages, dominate the conversation. The space of the city, as Joos illustrates, is a space of mobility and sociality, despite all the risks these activities entail. What is remarkable about this book is how it allows us to enter the lifeworlds of average urban Haitians and to grapple with the challenges they face in pursuing a life alèz (at ease). The book unfolds over five substantive chapters, each of which takes the reader into a scene of post-earthquake reconstruction and development. Chapter 1, “Developing Disasters: Dispossession and Industrialization in Northern Haiti,” covers the construction of Caracol Industrial Park on the northeastern coast of Haiti, where hundreds were promised employment and a modern way of life. Joos shows, however, that the US textile and clothing companies have not only failed to provide a living wage to factory employees but also destroyed the farmland that the now factory workers once cultivated. Once a most fertile region of Haiti, the Caracol region has become an industrial wasteland. [End Page 128] Chapter 2, “Industrial Framers: Abstract and Disciplinarian Landscapes in Post-earthquake Haiti,” focuses on the fallout of the construction of post-earthquake housing developments and asks how access to this new housing has been shaped by familial ties or kin relationships. The case under study is the state-led housing program of Village Lumane Casimir, a rural area outside Port-au-Prince that is named after the famous singer. Following the earthquake, a project took root to build an industrial park in the area. Through meeting with development agents and townsfolk, Joos reveals what is often hidden in policy reports—the social conflicts and jealousies that arise between developers and the to-be-developed. He also reveals how a regime of discipline, as Michel Foucault would name it, undergirds the workings of the development apparatus, showing that this regime takes place through policing workplace behavior and cultural practices. Regulating how to dress, talk, and carry oneself, as well as compelling employees to be on time, minimize breaks, limit trips to the bathroom, and not disturb their coworkers with tripotay (gossip), these corrections are part of what is at stake in “disciplining” the new workforce. I would welcome more discussion of how these tactics underscore a method of creating laborers specifically for industrial, factory work, which is quite different from the agricultural labor of the peasantry, where song and dance infuse the workday. Chapter 3, “State Interventions: Infrastructure and Citizenship,” scrutinizes post-earthquake development in Martissant, a district of Port-au-Prince that once housed political leaders and the professional classes. Following the collapse of the Duvalier family dictatorship...

Understanding taxi ridership with spatial spillover effects and temporal dynamics

In urban transportation systems, taxis are regarded as flexible, convenient, and time-saving. Taxi demand is affected by various built-environment factors and by the time of the day. Although many studies have investigated correlations between taxi demand and the built environment, the direct and spillover effects of built environment factors on taxi demand have not been examined at a fine spatial scale. To address this gap in the literature, this paper employs spatial econometric models using GPS-tracked taxi trips, mobile signaling data, and points of interest (POIs) to study taxi demand in Beijing at a 1-kilometer square grid resolution. The results show that, in the morning and evening peak hours, road network density has the strongest (positive) direct and indirect impact on taxi ridership. A relationship is also found between public transportation and taxi ridership: bus coverage has positive direct effects and insignificant indirect effects on taxi pick-ups and drop-offs, while subway coverage has negative indirect effects, suggesting that it may absorb taxi demand from surrounding grids. Results also indicate that various built-environment factors affect taxi demand differently at morning and evening peak times. This study reveals the complex nature of taxi ridership and has important implications for policymakers, transport planners, and other stakeholders in megacities around the world.

A double jeopardy: COVID-19 impacts on the travel behavior and community living of people with disabilities.

The COVID-19 pandemic response has had a significant impact on the general population’s ability to participate in their communities. Individuals with disabilities, an already socially disadvantaged population, are more vulnerable to and have likely been disproportionately impacted by COVID-19 response conditions. Yet, the extent to which daily community living activities of people with disabilities have been impacted is unknown. Thus, this study assesses their travel behavior and community living during the COVID-19 pandemic conditions compared with those of the general population during the same period.A web survey was conducted using Qualtrics’s online panel data (respondents included 232 people with disabilities and 161 people without disabilities). Regression models found that people with disabilities reduced their daily travel to a greater extent but at varying degrees, depending on the destination types and travel modes. Reductions in taxi rides (including ride-hailing services) were most significant among people with cognitive and sensory (vision and hearing) disabilities. By place type, cognitive disability was associated with a trip reduction for multiple destination types—grocery, restaurants, outdoor recreation, indoor recreation, and healthcare providers. Findings from this study could contribute to decision- and policy-making in planning, transportation, and community services during the remainder of the COVID-19 pandemic, in future major public health crises, as well as post-COVID, because the adjustments in travel behavior and community living might be longer-term.