Many people criticize the inequity of traditional taxi (TT) services and believe the entry of ride-hailing taxis (RT) can address the issue. However, this has been understudied in the literature. ...This paper aims to estimate the equity of TT and RT services during peak hours and to study how the entry of RT affects equity by analyzing trip data of TT and RT in New York City in 2010 and 2017 (before and after the entry of RT). First, we used the Lorenz curve and the Gini coefficient to estimate the equity of taxi services against population and employment. The results show that the equity of RT in 2017 is higher than that of TT and the equity of TT + RT in 2017 is higher than that of 2010. Mixed geographically weighted regression (MGWR) was applied to determine whether the relationships between taxi trips and population/employment would vary across different taxi zones. The coefficient of variation (CV) of local coefficients of population/employment is used as an indicator of equity. Results show that RT services were more equitable than TT services in 2017 and that the overall taxi service in 2017 was more equitable than that of 2010.
The distance from the origin or destination to or from the subway station is defined as the access or egress distance, which determines the service coverage of the subway station. However, little ...literature studies the distances at the station level, and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variation of the distances at the station level by using the mobile phone positioning data of more than 1.2 million anonymous users in Chengdu, China. First, this study proposes a method to extract the access and egress trips of the subway. Next, the ordinary least squares (OLS) regression models are carried out to select the significant explanatory variables. Finally, the geographically weighted regression (GWR) models are used to model the spatial variation relationship between the 85th percentile access/egress distances and the selected explanatory variables. The results show that different stations’ access/egress distances vary significantly in space. Hotel, residence, life, finance, road density, and mixed land use are found to be negatively correlated with distances, while education, 36–45 years old, male, and high education are positively correlated. In addition, the GWR model reveals that the influence of explanatory variables on access/egress distance varies from space to space. The results further promote the understanding of the existing system and provide a relevant reference for planners and transportation departments to optimize land use and public transportation planning.
•Develops a general framework for macroscopic pedestrian flow simulation, which is the first attempt to solve this problem under the physics-informed neural network (PINN) framework.•Proposes the ...reduced-order PINN to decompose the higher-order PDE and improve the model performance by reducing the derivative order of the auto-differentiation part.•Compares three PINN schemes, namely, vanilla PINN, the extended-variable PINN, and the reduced-order PINN.•Performs a sensitivity analysis on the key parameters of the PINN schemes while solving a particular macroscopic pedestrian flow model.
Given the importance of pedestrian flow simulation in reproducing pedestrian flows and optimizing pedestrian facilities, it is crucial to accurately simulate macroscopic pedestrian flows. Physics-informed neural network (PINN) is a recently proposed advanced scheme for solving partial differential equations (PDE) that can approximate the solution values by minimizing the initial values, boundary values, and residuals of the PDE. However, few studies have discussed how the performance of PINN changes in the face of macroscopic pedestrian flow equations with high-order derivatives and multiple variables. In this study, we propose to compare the performance of three schemes, i.e., the vanilla PINN, the extended-variable PINN (ev-PINN), and the reduced-order PINN (ro-PINN), by solving the macroscopic pedestrian flow equations coupled by the conservation equation and Eikonal equation in the steady-state and transient cases. The results show that the comparison of the PINN models under different hyperparameters indicates that the ro-PINN is the most stable in training, while the other two schemes have a certain degree of fluctuation in the face of different hyperparameters. Secondly, when the traditional numerical solution scheme is used as the reference solution, the ro-PINN solution works best in the steady-state and transient cases, and the solution results of different solution variables such as density and flow are closest to the reference solution, which can make the whole solution process easier and the solution results more accurate by reducing the derivative order in the automatic differentiation part and increasing the output dimension of the neural network. On the contrary, compared with vanilla PINN, ev-PINN not only did not improve the solution results, but even reduced the performance. Finally, we compare the running time of the three PINN schemes, and although increasing the output dimension and decreasing the order of PDE increase the computing time, it is not significant. Therefore, the ro-PINN can be used as an effective alternative model for simulating macroscopic pedestrian flow evolution.
Enhancing the efficiency and safety of the Intelligent Transportation System requires effective modeling and prediction of citywide traffic dynamics. Most studies employ convolutional neural networks ...(CNNs) with a 3D convolutional structure or spatio-temporal models with self-attention mechanisms to capture the spatio-temporal information of traffic distribution. Although 3D CNNs excel at capturing local contextual information, they are computationally complex due to the large number of parameters and cannot capture long-range dependence. By contrast, although self-attention mechanisms originally designed to address challenges in natural language processing can capture long-range dependence, their application to 2D image structures requires breaking down the inherent 2D context into a 1D sequence, increasing the computational complexity and neglecting the adaptability between local contextual information and channels. Accordingly, we propose a spatio-temporal visual attention neural network (STVANet), a novel spatio-temporal visual attention 2D CNN, which integrates a unique visual attention module with a large kernel attention (LKA) mechanism, a squeeze-and-excitation (SE) mechanism and a feedforward component to capture long-range dependence and channel information in urban traffic data while preserving the 2D image structure. LKA-based spatio-temporal attention networks extract spatial and temporal features from weekly, daily, and recent hourly periods, and aggregate them with weighted consideration of external features to make predictions. Evaluation of real-world datasets demonstrates STVANet’s superiority over baseline models, showcasing its potential in citywide traffic prediction.
An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people’s travel demands and promote the coordinated development of urban land. However, ...existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.
The ridesourcing service has taken a large portion of the taxi market in the past few years. Many studies have explored the influencing factors of traditional taxi and ridesourcing demand in ...different areas of the city. Few studies investigated the market share of ridesourcing in different regions of the city. Understanding the spatial distribution of the market share of ridesourcing service and the factors that determine this distribution could help government agencies understand the role that each type of service plays in the transportation system and evaluate the effect of different public policies on the two types of services. This paper studies this topic by constructing a panel fractional regression model based on the taxi trip data of New York City in 2017. Results show that the market share of the traditional taxi is declining while that of ridesourcing service is increasing. The market share of ridesourcing services is higher in remote areas, areas with low population density, low density of transportation facilities, low household income, and high proportion of young residents. It indicates that ridesourcing service could provide more equitable service by better serving the underserved areas while traditional taxi caters more to the high-demand areas.
•We explore built environment factors influencing market share of ridesourcing (RS).•A panel fractional regression model is used to analyze taxi trip data of NYC.•Remote areas, high ratio of low income and young residents are positively related.•Density of population, bus stop, road, and mixed land use are negatively related.•Results could help us understand the interaction of RS and traditional taxi.
Demand-responsive customized bus (DRCB) is a specific type of demand-responsive transit (DRT), which can flexibly customize services according to travel demand. However, few papers explored the ...influence factors on it. The purpose of this paper is to use the DRCB trip data from November 15 to December 15, 2020, in Xiongan New Area, China, to investigate the influence of built environment, weather, and time factors on DRCB ridership by using a negative binomial regression model. According to the spatiotemporal analysis, DRCB ridership is mainly concentrated in the peak hours of 07:00-09:00 and 17:00-20:00; the spatial distribution is primarily centered in areas such as government service center, railway station, and residential area. The model results show that scenic spot, governmental agency, morning peak period, and evening peak period are positively correlated with the DRCB ridership. Company, medical facility, visibility, rain, and weekend have a negative correlation with DRCB ridership. The results of this paper can provide insights for relevant companies to better understand the demand for DRCB and better plan the DRCB systems.
Many people criticize the unequal distribution of traditional taxis (TT) service and believe the advent of e-hailing taxis (ET) can bring equity to the public. However, this have not been rigorously ...studied by existing research. This paper aims to explore the spatial equity of taxi services and how the advent of ET affects the equity. Trip data of TT and ET in New York City in 2010 and 2017 was used for a case study. The spatial autoregressive models were used to estimate the spatial dependence of taxi service pick-ups, which could indicate the existence of inequality in taxi services. It was found that the spatial dependence of TT in 2010 was the highest and the spatial dependence of ET was the lowest in 2017 with other contributing factors like population and employment controlled. When ET appeared, the spatial dependence of the total taxis (TT+ET) trips in 2017 decreased compared with that in 2010, suggesting that the advent of ET increased the spatial equity of taxi services.
The rapid development of ride-hailing services has sparked debate about their role in urban transport. While there are several studies exploring impacts of ride-hailing services on transportation ...systems, little work has been done to study mode choice transitions of ride-hailing users. This paper investigates ride-hailing use in November 2018 in Chengdu, China. We use survey results to depict usage characteristics of ride-hailing trips and users. A binary logit model is utilized to investigate factors influencing mode transitions because we suspect that the previous mode has an important effect on the future mode choice (when ride-hailing services is banned or heavily restricted). Ride-hailing services caters specially to younger and educated respondents. Although total shares of car-based modes (drive alone, taxi, get a ride with friends /family) does not show obvious changes, many respondents would shift away from taxi towards transit, bike, and driving alone. Besides, some respondents who used transit previously would be more likely to choose taxi. Our study could offer insights for relative regulations and policies.
Traditional taxi has been criticized for underserving regions far away from downtown, leading to social inequity. However, there has been no evidence so far to support this argument. With the advent ...of e-hailing taxi services such as Uber and Lyft, it becomes possible to test this argument. Because e-hailing taxi trips are assigned to the drivers who are willing to undertake the rides, the e-hailing taxi trips plus the traditional taxi trips can be regarded as the true travel demand served by taxi. The proportion of e-hailing taxi trips to total taxi trips is used as an indicator of to what extent area is underserved by traditional taxi. All the origins of traditional taxi and e-hailing taxi trips in New York City from January 2015 to December 2017 are aggregated at the level of taxi zones, which is defined by the New York City Taxi Limousine Commission. Fractional Response Model (FRM) is used to identify factors that influence the proportion of e-hailing trips. Results show that the proportion of African-American, the proportion of households of income less than 50,000 dollars, the proportion of household of income between 100,000 to 200,000 dollars, road density, distance to the Midtown Center have positive impact on the proportion of e-hailing taxi trips, while the population density, subway station density, bus station density, existence of airport have negative impact on the proportion of e-hailing taxi trips.
