Trip Counts Research Articles

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.

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.

Understanding bike-sharing usage patterns of members and casual users: A case study in New York City

Shared bicycle travel has become an important travel mode for urban residents, and bike-sharing platforms are also booming in major cities worldwide. The bike-sharing platform provides users with systematic services: members refer to annual bike-sharing service subscribers and casual users refer to holders of a day pass or single ride ticket. Even though casual users account for a large share of ridership and revenue at bike-share systems in New York City (NYC), very little is known about the characteristics and preferences of casual users and how they compare to members. Based on the open-docked bike-sharing dataset from Citi Bike, we analyze the bike usage patterns of members and casual users in NYC, and how these patterns change in the face of the COVID-19 pandemic on a typical day level. We find that the COVID-19 pandemic has negatively influenced members' bike trip counts on weekdays; bike travel time increases for members during the pandemic and decreases for casual users after the pandemic. To make a profound study concerning spatial heterogeneities, we employ Gaussian Mixture Model (GMM) to cluster the spatiotemporal changes of the station-level bike usage and obtain four clusters for each user type. Combined with the Points of Interest (POI) information, we find that member-related cluster with commuting POIs is significantly affected by the pandemic, while leisure trips are the most severely affected for casual users. Compared with the central area, peripheral clusters with residential and religious POIs are less affected by the pandemic. According to our findings, new operational strategies such as flexible subscriptions can be developed to attract more users, maintain their stickiness, and improve the bike-sharing level of services.

Methodology for Monitoring Border Crossing Delays with Connected Vehicle Data: United States and Mexico Land Crossings Case Study

International trade is a critical part of the United States economy. Land border crossings between the United States and Mexico accounts for a large proportion of the USD 779 billion in trade between these two countries. Monitoring and managing the operations of these land border crossings is critical for ensuring efficient trade and providing appropriate security. This paper examines the opportunity to use connected vehicle data to monitor the travel time delay of passenger vehicles crossing the border for system level assessment across 26 border crossing locations over an analysis period of 25 days in August 2020. A sample size of 51,341 trips from the US to Mexico and 41,708 trips from Mexico to the US were used in this study. Furthermore, 97% trips to the US and 76% trips to Mexico experienced delays. The average delay was 34 min for trips to the US compared to only 2 min for trips to Mexico. In terms of the predictability of border crossing times, there was also substantial variation by direction. The interquartile range of vehicle delay from the US to Mexico was 2 min, while the interquartile range of delay for vehicles travelling from Mexico to the US was 46 min. Border crossings were also ranked using four performance metrics—trip counts, median delay, delayed trip counts and total delays in vehicle hours. Methods for summarizing delay trends by time of the day and day of the week to identify time windows of interest are also presented. Land border crossing operations have a significant influence on security and economic efficiency. We believe the techniques presented in this paper provide a scalable methodology for providing near real-time factual data on border crossing delays that provide important information for land border transport-managing stakeholders to make informed management decisions that balance security and economic efficiency.

Modelling the influence of socio-demographic attributes on students’ trip generation at Kwame Nkrumah University of Science and Technology (KNUST) campus

ABSTRACT This research investigates the influence of socio-demographic attributes on students’ trip generation on the campus of Kwame Nkrumah University of Science and Technology (KNUST). A cross-sectional and mixed-method approach were employed, collecting primary data from 113 students through an online questionnaire. Multiple linear regression analysis and ordinal logistic regression explored the relationships between generated trips and socio-demographic attributes, including age, household size, vehicle ownership, educational level, income, and trip purpose. Age and household size were significant influencers of trip generation in the multiple linear regression. The ordinal logistic regression showed that, males are 2.40 times more likely to generate higher trip counts than females. Individuals aged 16–21 are 0.11 times less likely to produce more trips compared to older age groups. Single-occupant households have a 0.09 times lower likelihood of generating more trips than households with two or more occupants. However, individuals attending morning and afternoon lectures, only morning lectures, and morning and evening lectures among other activities have substantially higher odds of producing more trips, with odds ratios of 22.14, 15, and 567, respectively, compared to those exclusively attending evening lectures. Recommendations include improving shuttle services, pedestrian infrastructure, and promoting sustainable mobility solutions tailored to student demographics.

Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation

This study aims to investigate the feasibility of using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) for modelling home-based trip generation in Salfit City, Palestine. The research compares the performance of these two methods and provides insights into their efficiency for different trip purposes. The methodology involves developing separate trip generation models for various purposes, including total daily household trips generated (HBALL), home-based work (HBW), home-based education (HBE), and other home-based trips (HBO). The underlying principles and implementation details of the ANFIS architecture are also discussed. Objective evaluation metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are used to assess the effectiveness of each model. The results indicate that ANFIS performs well for modeling HBALL and HBO trips, which exhibit more complex behavior with wider data ranges and higher average daily trip counts. Compared to the MLR approach, ANFIS shows improved accuracy and closer predictions. For instance, when modelling HBALL trips, ANFIS achieved an RMSE of 1.4880, while MLR resulted in 1.7112, representing a reduction of 13.04%. However, for HBW and HBE trip purposes, where behavior is less complicated, MLR appears to be sufficient. The R-squared values obtained with MLR are sufficiently high to capture most of the trip variations, and the results between the two approaches are closely comparable. The R-squared value for MLR in HBE trips was almost identical to that of ANFIS, which was 96.65%. Similarly, for HBW trips, MLR achieved an R-squared value of 90.36%, while ANFIS performed slightly better with an R-squared value of 92.74%. In conclusion, ANFIS shows promise for modelling systems with complex behavior, while MLR remains a suitable option for less complicated scenarios. The study emphasizes the importance of exploring different modelling techniques in transportation research to identify the most appropriate approach for specific cases.

Estimation of flow trajectories in a multi-lines transportation network

Characterizing a public transportation network, such as an urban network with multiple lines, requires the origin–destination trip counts during a given period. Yet, if automatic counting makes the embarkment (boarding) and disembarkment (alighting) counts in vehicles known, it often happens that pedestrian transfers between lines are harder to track, and require costly and invasive devices (e.g., facial recognition system) to be estimated. In this contribution, we propose a method, based on maximum entropy and involving an iterative fitting procedure, which estimates the passenger flow between origins and destinations solely based on embarkment and disembarkment counts. Moreover, this method is flexible enough to provide an adaptable framework in case additional data is known, such as attraction poles between certain nodes in the network, or percentages of transferring passengers between some lines. This method is tested on toy examples, as well as with the data of the public transportation network of the city of Lausanne provided by its Transportation Agency (tl), and gives arguably convincing estimations of the transportation flow.

Simulation-Based Origin-Destination Matrix Reduction: A Case Study of Helsinki City Area

Estimation of a travel demand in a form of origin-destination (OD) matrix is a necessary step in a city-scale simulation of the vehicular mobility. However, an input data on travel demand in OD matrix may be available only for a specific set of traffic assignment zones (TAZs). Thus, there appears a need to infer OD matrix for a region of interest (we call it ‘core’ area) given OD matrix for a larger region (we call it ‘extended’ area), which is challenging as trip counts are only given for zones of the initial region. To perform a reduction, we explicitly simulate vehicle trajectories for the extended area and supplement trip values in ‘core’ TAZs based on the recorded trajectories on the border of core and extended areas. To keep validation results consistent between extended and core simulations, we introduce edge-based origin-destination assignment algorithm which preserves properties of traffic flows on the border of the core area but also keeps randomness in instantiating simulation for the core area.
 The experimental study is performed for Helsinki city area using Simulation of Urban MObility (SUMO) tool. The validation was performed using DigiTraffic data from traffic counting stations within the city area for workdays of autumn 2018. Validation results show that the reduced OD matrix combined with edge-based OD assignment algorithm keeps the simulated traffic counts in good agreement with results from the extended area simulation with average MAPE between observed and simulated traffic counts equal to 34%. Simulation time after reduction is equal to 20 minutes compared to 6 hours for the extended OD.

Identifying sinks and sources of human flows: A new approach to characterizing urban structures

Human flow data are rich behavioral data relevant to people’s decision-making regarding where to live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy to understand massive relational data, and datasets have often been reduced merely to the statistics of trip counts at destinations, discarding relational information from origin to destination. In this study, we propose an alternative center identification method based on human mobility data. This method extracts the scalar potential field of human trips based on combinatorial Hodge theory. It detects not only statistically significant attractive locations as the sinks of human trips but also significant origins as the sources of trips. As a case study, we identify the sinks and sources of commuting and shopping trips in the Tokyo metropolitan area. This aim-specific analysis leads to a combinatorial classification of city centers based on the distinct aspects of human mobility. The proposed method can be applied to other mobility datasets with relevant properties and helps us examine the complex spatial structures in contemporary metropolitan areas from the multiple perspectives of human mobility.

Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike‐sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.

Long-Distance Travel Impacts of COVID-19 Across the United States

Using over a thousand Americans’ population-weighted responses to a long-distance travel survey, this paper examines reductions in trips over 75-miles (one-way) in 2020, during the pandemic, versus behaviors in 2019. Negative binomial models of trip counts suggest that people age 25 to 64 took 0.20 fewer annual long-distance business trips during the pandemic, but people age 65 and older took 0.45 fewer business and 0.57 fewer non-business long-distance trips, on average. Household income was not a key predictor for long-distance non-business-trip-making during the pandemic, but was important in predicting long-distance trip rates for business purposes (both before and during the pandemic) and for non-business trips pre-pandemic.

Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea

The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.

Toward learning dynamic origin-destination matrices from crowd density heatmaps

Knowing the origins and destinations of pedestrians’ paths is key to the initialization of crowd simulations. Unfortunately, they are difficult to measure in the real world. This is one major challenge for live predictions during events such as festivals, soccer games, protest marches, and many others. Sensor data can be used to feed real-world observations into simulations in real-time. As input data for this study, we use density heatmaps generated from real-world trajectory data obtained from stereo sensors. Density information is compact, of constant size, and in general easier to obtain than e.g., individual trajectories. Therefore, the information limitation improves the applicability to other scenarios. We include the absolute pedestrian trip counts from origins to destinations during a brief time interval in an OD matrix, including unknown destinations due to sensor errors. Our goal is to estimate these OD matrices from a series of density heatmaps for the same interval. For this, we compute the ground truth OD matrices and density heatmaps using real-world trajectory data from a train station. We employ linear regression as a statistical learning method for estimation. We observe that the linear share of the relationship between density and OD matrix is estimated successfully. Nevertheless, a portion of the data remains that cannot be explained. We attempt to overcome this difficulty with random forest as a nonlinear model. The results indicate that both a linear and a nonlinear model can estimate some features of the OD matrices. However, there is no clear winner in terms of the chosen metric, the R 2 score. Overall, our findings are a strong indicator that OD matrices can indeed be estimated from density heatmaps extracted automatically from sensors.

Ride-hailing through the COVID-19 Pandemic in New York City

We explored the effects of the COVID-19 pandemic on ride hailing trips in New York City between February 1, 2019 and July 31, 2021. Using negative binomial regression models, we quantified the effects of COVID-19 indicators on daily ride hailing trip counts. We found that a 1% increase in the number of new cases and vaccines administered were associated with 16.0% decrease and 0.8% increase in trip counts, respectively. Since the initial drop of 258.2% during the stay-at-home phase of the pandemic, trip counts recovered in the subsequent phases albeit still lower than pre-pandemic levels at -85.0% and -38.7% during the reopening and vaccination phases, respectively.

Prediction of Bike-Sharing Trip Counts: Comparing Parametric Spatial Modeling Techniques to a Geographically Weighted Xgboost Algorithm

Prediction of Bike-Sharing Trip Counts: Comparing Parametric Spatial Modeling Techniques to a Geographically Weighted Xgboost Algorithm

Dynamic Modal Split Incorporating Trip Chaining: A Parsimonious Approach to Mode-Specific Demand Estimation

Dynamic mode choice is essential to understand the potential effectiveness of policies aiming to achieve desirable modal split targets or to manage the demand for resource-limited systems such as shared mobility services. In this paper, we propose an estimation of dynamic modal split for work-related trips, including mode- and time-specific costs, with activity participation based on utility maximization. In order to obtain an accurate profile while remaining at an aggregate level, three types of work activities are described (full time, morning and afternoon shift). The estimated modal split concerns motorized vehicles, soft modes but also train and urban public transport. Based on utility maximization principles, the accumulated utility is formulated within a departure time choice model. A Markov Chain Monte Carlo procedure is used to evaluate the marginal utility function parameters which are used in a joint departure time and mode choice evaluation. Mode specific travel speed for each time of the day is used to estimate also travelled distance distribution per mode. The methodology is applied and tested, using data collected in Ghent in 2008. 16.749 work-related trips have been considered in a simplified estimation where two successive trips are constrained to be done with the same mode. This methodology is characterized by low data requirements and the model is shown to be flexible to include all available type of information in order to refine or accelerate the estimation. The proposed method is easy to implement using only dynamic trip counts, without the need for simulation or traffic assignment.

An in-depth spatiotemporal analysis of ride-hailing travel: The Chicago case study

With the rapid increase in ride-hailing, a need to better understand and regulate the industry arises. Conflicting results have been published by researchers and policy makers regarding ride-hailing’s impact on congestion, public transit, and other aspects of traffic systems. One of the obstacles to studying ride-hailing is the lack of granular operational data over a relatively long period of time. An efficient system that can coordinate ride-hailing and other travel modes to better serve travel needs and minimize negative impacts on the transportation system requires sufficient ride-hailing trip-by-trip data and an understanding of ride-hailing trip patterns. Such patterns include the times that travelers use ride-hailing, where they are traveling from and to, how weekend and weekday ride-hailing trips differ, etc. This paper analyzes a year’s worth of ride-hailing trip data from the Greater Chicago Area, which included detailed time, date, trip length, origin, and destination information to study the ride-hailing trip patterns. More than 104 million trips were analyzed. For trip rates, the results show that the total number of trips remained stable over the year. People tend to use ride-hailing more on weekends compared to weekdays. Specifically, weekend ride-hailing trip counts (per day) are, on average, 20% higher than weekday trip counts. Spatial patterns of ride-hailing trips were analyzed in ArcGIS. The results show that longer ride-hailing trips, which have their origins and destinations located in different census tracts, are more statistically correlated to factors such as income, education level, and average transit commuting time in the area. They are also more spatially clustered compared to their shorter counterparts, which have their origins and destinations located in the same census tracts. Longer ride-hailing trips are more clustered around the central business district (CBD) and gradually become sparser while moving further away from the CBD. The results of this paper will help policy makers and transportation administrators better understand the nature of ride-hailing trips, which in turn allows for better regulation and guidance for the ride-hailing industry.

Modeling outdoor thermal comfort along cycling routes at varying levels of physical accuracy to predict bike ridership in Cambridge, MA

The Universal Thermal Climate Index (UTCI) has been linked to outdoor activity patterns and used to evaluate the effectiveness of urban interventions to improve thermal comfort. This study investigates how simulating the urban environment at increasing levels of physical accuracy impacts UTCI values along three cycling routes in Cambridge, Massachusetts. Baseline UTCI values are estimated using a local weather file, and the following increments in physical accuracy are considered: wind-scaling, shading from buildings, shading and cooling from trees, computational fluid dynamics simulations for wind speeds, and simulated surface temperatures. With bike ridership data from Bluebikes, Boston's bike-sharing program, the relationship between bike ridership patterns and UTCI values along each route is studied. Supervised machine learning models are applied to predict bike ridership based on UTCI and other predictors.UTCI simulation results show that incorporating the various increments of accuracy influences hourly UTCI values at urban areas and exposed areas differently. Incorporating local wind speeds is especially impactful for urban areas. The statistical models trained to predict hourly bike trip counts based on UTCI and other demand and weather predictors achieved a root-mean-squared error of 1.06 trips. 47% of predictions were correct, and an additional 42% of predictions were off by 1 trip.This study demonstrates the importance of spatial refinement in simulating UTCI, and motivates future research into efficient simulation methods or rules-of-thumb for deriving spatial-temporal UTCI values. Future work into building a robust predictive model would motivate the design of thermally comfortable environments for human-powered transportation in cities.

Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data

BackgroundRidesharing services (eg, Uber, Lyft) have facilitated over 11 billion trips worldwide since operations began in 2010, but the impacts of ridesharing on motor vehicle injury crashes are largely unknown.—MethodsThis...

Improving transportation impact analyses for subsidized affordable housing developments: A data collection and analysis of motorized vehicle and person trip generation

Transportation impact analyses begin with a trip generation estimation process—estimating motorized vehicle and person trip counts coming and going from the proposed site. Data commonly used is often insensitive to urban contexts (such as employment densities) and socioeconomic conditions. This insensitivity results in sometimes exaggerated estimates, an increase associated transportation impact fees, and a need for additional mitigation of impacts which may further hinder land development. In this study, we collected and analyzed person and motorized vehicle count data from 26 affordable housing developments in Los Angeles and San Francisco. Counts were regressed upon site and built environment characteristics known to influence site-level travel behavior (e.g., parking supply, employment density), and regressions were validated using externally collected data. The findings indicate the average square footage of dwelling units, parking ratios, and nearby retail employment densities to be important predictors. The findings also indicate that increasing the parking supply from one space to two for each dwelling unit will result in a significant predicted increase of approximately 0.26 and 0.18 motorized vehicle trips per dwelling unit for AM and PM peak periods, respectively. These findings reiterate the need for trip generation methodologies sensitive to the built environment and sociodemographics.