Taxi Trips Research Articles

Understanding walking behaviour during COVID-19: Binary Logit and ANN approach

The COVID-19 pandemic had severe impacts on society. It negatively affected many sectors, and transportation is one of those. Naturally, the walking trip behavior of individuals was also altered. This study aims to investigate the changes in walking trips of individuals with two models: Binary Logit (BL) and Artificial Neural Networks (ANN). An online survey was conducted with 387 individuals. BL model investigated if respondents' walking trips would increase during and after the pandemic. On the other hand, ANN models were developed to determine the significant factors in changes in walking behavior. Results indicate that ANN models capture more factors than BL models. Demographics and attitudes towards public transportation, taxi, and walking trips during the pandemic are found to be effective in walking behavior changes. Policies can be made to increase public transit ridership, and infrastructure for walking can be improved. Future research suggestions are given.

Game Day Traffic and Sports: Evidence From Yankee Stadium in the Bronx

This article investigates the impact of sports games on taxi trip times near a stadium in the Bronx. The traffic outcome variable comes from data on individual taxi trips in New York City. These trip-level data contain information on pick-up and drop-off times and locations, and trip distance. We analyze over 280,000 taxi trips on weekday evenings between March and September 2019. Under the assumption that taxi trips represent traffic conditions in the city, the results indicate that travel time increases on a game day. Travelers experience 1.3% longer travel times on game days. Game-related traffic increases travel time by 6.3% for the trips that pass through the stadium area, and by 17% for trips before the game starts. Local economic value of lost time from games amounts to about $2 million per year. Governments should consider these negative externalities when providing subsidies for new sports facility construction.

Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior

The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.

Complements or competitors? Equity implications of taxis and ride-hail use in Chicago

Taxis have provided on-demand mobility for decades, including to people earning the lowest incomes. However, studies reveal gaps in taxi service across many US cities due to factors such as fleet size limitations, information asymmetry, and discriminatory practices. Since 2010, the emergence of ride-hail services such as Uber and Lyft has revolutionized the transportation landscape, offering faster and more affordable alternatives to traditional taxis. Yet it remains unclear the extent to which ride-hail services may fill spatial-temporal gaps in taxi services or how car access shifted after the arrival of ride-hail services. Additionally, taxis may still play an important role in car access for people who rely on cash payments. This study uses longitudinal trip-level ride-hail and taxi data in Chicago, IL, to examine the shifting trip patterns across the two modes. We compared the spatial-temporal pattern of taxi and ride-hail trips, as well as the share of taxi trips paid for in cash across three periods; we then developed spatial regression models to investigate what factors relate to taxi cash payments. Results show that ride-hail services may, directly and indirectly, fill taxi service gaps. Additionally, population density, land use mix, and the proportion of people of color and zero-car households are positively related to the share of taxi trips paid in cash, while the number of ride-hail trips and banking density are negatively associated with the percentage of cash payments. Policymakers should consider the evolving interplay between taxi and ride-hail services and the significance of cash payments to ensure equitable access to transportation options for individuals without cars.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

The Application of the Piecewise Linear Method for Non-Linear Programming Problems in Ride-Hailing Assignment Based on Service Level, Driver Workload, and Fuel Consumption

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model’s performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model’s potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly.

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

ABSTRACT A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis

Understanding traveler mobility in cities is significant for urban planning and traffic management. However, most traditional studies have focused on travel mobility in a single traffic mode. Only limited studies have focused on the travel mobility associated with multimodal transportation. Subways are considered a green travel mode with large capacity, while taxis are an energy-consuming travel mode that provides a personalized service. Exploring the relationship between subway mobility and taxi mobility is conducive to building a sustainable multimodal transportation system, such as one with mobility as a service (MaaS). In this study, we propose a framework for comparatively analyzing the travel mobilities associated with subways and taxis. Firstly, we divided taxi trips into three groups: competitive, cooperative, and complementary. Voronoi diagrams based on subway stations were introduced to divide regions. An entropy index was adopted to measure the mix of taxi trips. Secondly, subway and taxi trip networks were constructed based on the divided regions. The framework was tested based on the automatic fare collection (AFC) data and global positioning system (GPS) data of a subway in Beijing, China. The results showed that the proportions of taxi competition, taxi cooperation, and taxi complements were 9.1%, 35.6%, and 55.3%, respectively. The entropy was large in the central city and small in the suburbs. Moreover, it was found that the subway trip network was connected more closely than the taxi network. However, the unbalanced condition of taxis is more serious than that of the subway.

Self-paced Gaussian-based graph convolutional network: predicting travel flow and unravelling spatial interactions through GPS trajectory data

ABSTRACT Spatial interaction research is particularly important for geographical analyses, as it plays a crucial role in extracting travel patterns. However, previous studies on spatial interactions have not adequately considered regional population variations over time, resulting in insufficiently precise travel predictions. Moreover, the threshold of spatial correlations is difficult to determine. Existing studies have assumed fully connected spatial correlation matrices, which is not realistic. To address these limitations, we proposed the Self-paced Gaussian-Based Graph Convolutional Network (SG-GCN) to automatically estimate the threshold of spatial correlations for travel flow predictions. It incorporates a temporal dimension into spatial relationship matrices to enhance the accuracy of vehicle flow predictions. In particular, Gaussian-based GCN identifies patterns in a time series of regional flows, enabling more precise capturing of spatial relationships while fusing node and edge features. Building on this model, self-paced contrastive learning automatically sets thresholds to determine the presence or absence of spatial relationships. The model's performance was verified through two empirical case studies conducted in New York City, USA, and Ningbo, China, using 2.8 million bicycle-sharing records and 1.25 million taxi trip records, respectively. The proposed model helps delineate mobility patterns in cities of varying scales and with different modes of transportation.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

A Sensor-Based Simulation Method for Spatiotemporal Event Detection

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.

Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods

Improving the estimation of origin–destination (O-D) travel times poses a formidable challenge due to the intricate nature of transportation dynamics. Current deep learning models often require an overwhelming amount of data, both in terms of data points and variables, thereby limiting their applicability. Furthermore, there is a scarcity of models capable of predicting travel times with basic trip information such as origin, destination, and starting time. This paper introduces novel models rooted in the k-nearest neighbor (KNN) algorithm to tackle O-D travel time estimation with limited data. These models represent innovative adaptations of weighted KNN techniques, integrating the haversine distance of neighboring trips and incorporating correction factors to mitigate prediction biases, thereby enhancing the accuracy of travel time estimations for a given trip. Moreover, our models incorporate an adaptive heuristic to partition the time of day, identifying time blocks characterized by similar travel-time observations. These time blocks facilitate a more nuanced understanding of traffic patterns, enabling more precise predictions. To validate the effectiveness of our proposed models, extensive testing was conducted utilizing a comprehensive taxi trip dataset sourced from Santiago, Chile. The results demonstrate substantial improvements over existing state-of-the-art models (e.g., MAPE between 35 to 37% compared to 49 to 60% in other methods), underscoring the efficacy of our approach. Additionally, our models unveil previously unrecognized patterns in city traffic across various time blocks, shedding light on the underlying dynamics of urban mobility.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Predicting taxi unmet trip demand

The purpose of this project is to predict the number of taxi trip that goes unmet in NYC by using available trip data generated by Taxi Cabs. The expected outcome from this project is to identify the areas in NYC that experiences unmet demand and to identify socio-economic features that impact trip demand. The project was broken down into 2 phases. To account for daily trip trends the study is broken down in 7 periods on Monday to Saturday and 4 periods on weekends. In the rst phase 3 approaches were developed to identify census tract where the demand of taxi is not met. The rst approach was to compare the monthly total pickup and drop-of counts. The second approach identied the underserved census tracts by nding those with the least number of vacant taxis within a given time duration. In the third approach, the rate of change Uber pickup was compared with the rate of change of combined pick up of all taxi services over a 6 months period. The nal output was: 358 unmet trip demand CT that had pick-ups to drop-offs ratio lower than 25%, Less than 2 vacant taxi a minute and a Uber growth rate more than 4%. These areas were in North-east Queens, South Brooklyn and North of the Bronx. In Phase II, the socio-economic features were ranked to understand their inuence on taxi demand. Out of the 11 selected features the top 5 features were neighborhoods with high number of offices and employees, residential areas, income and rent. Using the 11 features as predictors and pick-up from CT with satisfactory as target variable a model was developed to predict demand for underserved areas. The model predicted overall 92,000 trips per day goes unmet in NYC which is 4.2% of the total current daily trips.

An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival

Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction.

SPATIAL INTERACTION ANALYSIS OF URBAN FUNCTIONAL DISTRICTS BASED ON TAXI TRAJECTORIES

Abstract. The functional differences among various components of a city drive the circulation of logistics, human flows, and energy flows within the city. Qualitatively and quantitatively analyzing the spatial interactions among urban functional districts contributes to understanding the spatial structure of it and formulating more efficient urban planning strategies. Based on taxi trips, this paper compares spatial interactions between the Xudong Commercial District and the Zhongnan Road Commercial District in Wuhan, respectively, with other functional districts. Firstly, the spatial interaction based on taxi trips between these two commercial districts, acting as either the origins or destinations for taxi trips, with various functional districts was visualized. Subsequently, we analyzed the intensity and distance of the spatial interactions based on taxi trips during each typical time period (8:00-9:00, 18:00-19:00, and 20:00-21:00 on weekdays; and 9:00-10:00, 19:00-20:00, and 22:00-23:00 on weekends). These results can serve as reference for further research on the influence range, development planning, and transportation optimization of these commercial districts.

Spatiotemporal variability of ride-pooling potential – Half a year New York City experiment

Ride-pooling systems, despite being an appealing urban mobility mode, still struggle to gain momentum. While we know the significance of critical mass in reaching system sustainability, less is known about the spatiotemporal patterns of system performance. Here, we use 1.5 million NYC taxi trips (sampled over a six-month period) and experiment to understand how well they could be served with pooled services. We use an offline utility-driven ride-pooling algorithm and observe the pooling potential with six performance indicators: mileage reductions, travellers' utility gains, share of pooled rides, occupancy, detours, and potential fleet reduction. We report distributions and temporal profiles of about 35 thousand experiments covering weekdays, weekends, evenings, mornings, and nights. We report complex spatial patterns, with gains concentrated in the core of the network and costs concentrated on the peripheries. The greatest potential shifts from the North in the morning to the Central and South in the afternoon. Offering pooled rides at the fare 32% lower than private ride-hailing seems to be sufficient to attract pooling yet dynamically adjusting it to the demand level and spatial pattern may be efficient. The patterns observed in NYC were replicated on smaller datasets in Chicago and Washington, DC, the occupancy grows with the demand with similar trends.

Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services and understand how socio-demographic and urban land use attributes affect the usage of two services in temporal and spatial dimensions. We employ the local regression models named geographically and temporally weighted regression (GTWR) models that can capture the spatiotemporal nonstationarity between variables as the primary analysis tool. Four GTWR models that predict the average hourly taxi or ride-hailing trip volume on weekdays or weekends for each community area are carefully constructed with R^2 all above 0.98. The nonstationarity test shows all variables exhibit extra local variations, indicating the necessity of using the GTWR models to explore the attributes’ spatiotemporal impacts. Spatial error models (SEM) and ordinary least square (OLS) models, global regression models, are also built as comparisons and benchmark references. The differences between global and local regression models are illustrated through clustering analysis. To compare and contrast the usage patterns of ride-hailing and taxi services, we analyze the temporal-spatially varying coefficients of GTWR models. Interpretations are provided for our interesting findings, including the strong trip generation power of the population in the South Side area, the attitude of white people towards taxi services is positive but ambiguous towards ride-hailing services, young people's opposing behaviors towards taxi services on weekdays and weekends, and the opposite relationship between transit and taxi or ride-hailing in certain areas. Based on these key findings, we offer some planning and operational suggestions, such as developing mobile apps, implementing price-related strategies, strengthening partnerships with local businesses, building multi-modal transportation, clarifying customer segmentation, providing auxiliary services, etc.

Generalized kernel two-sample tests

Summary Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space mainly target specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power improvements over existing kernel two-sample tests for a wide range of alternatives. We also propose alternative testing procedures that maintain high power with low computational cost, offering easy off-the-shelf tools for large datasets. The new approaches are compared to other state-of-the-art tests under various settings and show good performance. We showcase the new approaches through two applications: the comparison of musks and nonmusks using the shape of molecules, and the comparison of taxi trips starting from John F. Kennedy airport in consecutive months. All proposed methods are implemented in an R package kerTests.

Merging transport network companies and taxis in Curitiba’s BRT system

AbstractThe Brazilian city of Curitiba became known around the world for pioneering bus rapid transit (BRT) in the 1970s. Five decades later, public transport ridership is declining on the city’s bus-based system. One-person car trips and car ownership are soaring, and services provided by transport network companies rapidly proliferate and then disappear as congestion worsens and expands across the road network. This was the macro-scale scenario for mobility and modal trends in Curitiba until COVID-19 brought things to a screeching halt in 2020. The widespread use of information and communication technologies has allowed taxi and car ride-hailing transport network schemes to emerge while blurring the lines between public and private and individual and collective transport, locally as well as globally. In 2016, transport network company systems, apps, private cars, services, drivers and passengers disrupted Curitiba’s longstanding and well-regulated taxi system and market for licenses. In 2023, hailing a cab or a shared ride feels and costs the same for passengers (now customers). This study investigates whether these actors and technologies compete with or complement each other in this city, locating and quantifying the benefits for passengers of merging taxi and car ridesharing with the BRT system as first- and last-mile transport to and from BRT corridors. We developed mobile information and communication technologies and acquired, processed, and analyzed millions of data points for passenger location on BRT, ordinary bus, and taxi trips at the city scale. The shareability index for Curitiba’s taxi or car rides was calculated, demonstrating that 60% of all taxi trips have the potential to serve as first- and last-mile transport solution to and from the BRT terminals, stations, and corridors and that nearly 40% of taxi trips both originate and end near (< 500 m) this BRT system infrastructure. By envisioning how transport network companies could merge into the built environment thanks to urban transport digitization, we have developed a model for integrating public transport with the analytic framework of transport network companies that could be deployed in other cities with similar challenges related to public transport, sociotechnical arrangements, system complexity, policymaking, and planning.