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Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city. Recent advances primarily rely on deep reinforcement learning (DRL) to directly learn the optimal dispatching policy. These works, however, are still not sufficiently efficient because they overlook several pieces of valuable context information. As a result, they may generate quite a few improper actions and introduce unnecessary coordination costs. To improve existing works, we present COX - a context-aware taxi dispatching approach that incorporates rich contexts into DRL modeling for more efficient taxi reallocations. Specifically, rather than simply dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network graph into zones for practical taxi dispatching. In addition, COX comprehensively analyzes zone-level taxi demands and supplies through accurate taxi demand prediction and timely updates of taxi statuses. COX improves the DRL modeling by integrating these derived contexts, e.g., state representation with complete demand/supply data and sequential action generation with full coordination among idle taxis. In particular, we implement an environment simulator to train and evaluate COX using a large real-world taxi dataset. Extensive experiments show that COX outperforms state-of-the-art approaches on various performance metrics, e.g., on average improving the total order values by 6.74%, while reducing the number of unserved taxi orders and passengers' waiting time by 4.92% and 44.84%, respectively.

This paper traces the plunge and rebound of the taxi market in Shenzhen, China through the COVID-19 lockdown. A four-week taxi GPS trajectory data set is collected in the first quarter of 2020, which covers the period of lockdown and phased reopening in the city. We conduct a spatiotemporal analysis of taxi demand using the data, and then select taxis that continued to operate through the analysis period to examine whether and how they adjusted operational strategies. We find, among other things: (i) the taxi demand in Shenzhen shrank more than 85% in the lockdown phase and barely recovered from that bottom even after the city began to reopen; (ii) the recovery of taxi travel fell far behind that of the overall vehicle travel in the city; (iii) most taxis significantly cut back work hours in response to the lockdown, and many adjusted work schedule to focus on serving peak-time demand after it was lifted; (iv) taxi drivers demonstrate distinct behavioral adaptations to the pandemic that can be identified by a clustering analysis; and (v) while the level of taxi service dropped precipitately at the beginning, it quickly rebounded to exceed the pre-pandemic level, thanks to the government’s incentive policy. These empirical findings suggest (i) incentives aiming at boosting supply should more precisely target where the boost is most needed; (ii) the taxi market conditions should be closely monitored to support and adjust policies; and (iii) when the demand is severely depressed by lockdown orders or when the market is oversupplied, taxi drivers should be encouraged and aided to use more centralized dispatching modes.

The proliferation of ridesourcing services has raised both hopes and concerns about their role in cities. The impacts of ridesourcing services are complex and multi-faceted. Through reviewing the literature, this study aims to identify the social, economic, and environmental impacts of these services and highlight opportunities and challenges that lay ahead of them for resolving issues related to urban transportation. According to the results, ridesourcing services offer safe modes of transport that provide convenient mobility options, improve transit availability in disadvantaged and remote areas, and respond to taxi demand fluctuations. They can create new job opportunities by employing new human resources that have not been used before, provide flexible working hours for drivers, and are more efficient than taxi cabs. These services provide other opportunities to extend or complement public transit, reduce car ownership and congestion, and minimize parking supply. However, they are criticized for unfair competition with traditional taxis, limited compliance with social legislation, and lack of affordability. They are not available in all places and exclude some vulnerable and socially disadvantaged groups. Labor rights are not secure in this industry, and driver income is not stable. Finally, there is also evidence showing that, in some cases, they contribute to the growth of VMT, energy use, greenhouse gas emissions, and congestion in cities.

Background Taxi services have played an essential role in the transport system as they contribute to urban mobility. With the rapid development of information and communication technologies, ride-hailing services, one of the typical sharing economy forms of road transport, have become increasingly popular. The uptake of app-based technologies that support the sharing economy for transport is considered to threaten the future of traditional taxi services directly. Aim This study aims to compare the direct and indirect effects of factors such as perceived benefits of booking method, perceived safety, involvement and satisfaction on the loyalty of passengers to ride-hailing and traditional taxi services. Method A structural equation model of the relationships among these constructs was tested using data collected from 545 respondents (263 ride-hailing passengers and 282 traditional taxi passengers) who have used these services at least once a month in Vietnam. Results The results have confirmed that there was a strong relationship between satisfaction and loyalty for ride-hailing as well as traditional taxi services. It was also evident that the variable describing the perceived benefits of booking method was the second strongest factor influencing traditional taxi passengers’ loyalty while the perception of safety was the second most important determinant of ride-hailing passengers’ loyalty. Conclusions Results from this investigation offer insights for the development of strategies aiming at increasing the loyalty of ride-hailing as well as traditional taxi users. By focusing on the strengths and improving the weaknesses of each transport service, these two services can be complementary and co-existing without cannibalising each other. In turn this will lead to a transport system capable to serve all types of users.

The transport department of urban cities always needs to keep abreast of sustainable developments such as in Port Said city that is an important urban city in Egypt. Bike routes planning mission is not an easy occupation, especially in developing countries. Mixed traffic is the main shape of the transportation system in most of their systems. The increase of the bike user's percent is an expected objective and be one of the modern sustainable transport solutions. On another side, a lot of problems and accidents had been occurred according to bike crossings among other transport vehicles; cars, buses, taxis, and others. This paper aims at studying the introduction of the bike route's effectiveness. It concludes the driver's reaction to a definite planning scenario and the optimistic effect of the modal change on the objective function that has been assumed by attaching a suitable time-saving for bike users using the Artificial Neural Network ANNs Approach. It shows the effect of bike speed change on the route using a greedy algorithm. The study designated only four streets from seven selected streets to be suitable routes for bike routes introduction. The average bike speed is predicted to increase from 1.5 km per hour to be 2.4 km per hour after introducing the bike routes.

Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis should follow in order to minimize their idle times are hard to calculate; they depend on complex effects like passenger demand, traffic conditions, and inter-taxi competition. Here we explore if reinforcement learning (RL) can be used for this purpose. Using real-world data from three major cities, we show RL-taxis can indeed learn to minimize their idle times in different environments. In particular, a single RL-taxi competing with a population of regular taxis learns to out-perform its rivals.

Shared taxis, mini-buses, and motorcycles have been the conventional means of intra-city public transportation in most parts of Africa. In recent years, auto-rickshaws are fast becoming an alternative and preferred form of public transport in many African cities. This article adopts descriptive statistics to analyze the demographic characteristics of 429 auto-rickshaw operators in Kumasi and logistic regression analysis to understand the association between these demographic characteristics and operational attributes and challenges. The study found that auto-rickshaw operators in Kumasi possess gender, age, and educational characteristics similar to other transport operators in Ghana and elsewhere. Auto-rickshaw operators, however, differ from others in terms of their origin characteristics, as they are mostly migrant workers. Findings also revealed that the income generated by an auto-rickshaw operator is influenced by his training, union membership and type of transport activity. There is a general lack of compliance with road safety and traffic regulations irrespective of the educational level and training of auto-rickshaw operators. Nevertheless, operators who are relatively educated are more likely to maintain their auto-rickshaws than the less educated ones. The article recommends, among others, the strengthening of driver licensing and law enforcement regimes in Ghana while making formal training and education for auto-rickshaw operators mandatory.

Taxi drivers face many problems every day including safety issues. The tendency to quickly transport passengers to their destinations for more income has resulted in dangerous driving behaviors leading to traffic violations. So, taxi drivers need appropriate support and training programs to improve safety and reduce the risk of crashes. Implementing different support and safety training programs requires an effective management system. There is a dearth of research on the safety issues of taxis from the perspective of taxi organization managers. This study aims to evaluate the safety issues of taxi transport management through a case study of the Tehran Taxi Organization. A questionnaire survey was conducted with 22 regional managers and 20 transportation specialists of the Tehran Taxi Organization. Issues related to taxi drivers, roads and road users, vehicles, and management systems were evaluated in the questionnaire. Participants determined the relevance level and priority ranking of each question. The level of agreement was then tested using the Kendall concordance test. According to the results, the use of GPS was selected as the best in-vehicle monitoring system that can be used to evaluate drivers in the fleet. Participants believed that passengers’ loading and unloading had the most risk for taxi users. The start-inhibit technology to detect open doors was unanimously evaluated as an efficient technology for taxi safety. With respect to educating taxi users, starting education in schools had the most relevance and priority. Recommendations for increasing the safety of taxis include the use of GPS in taxis to monitor and evaluate drivers, receiving crash reports from police and submitting monthly safety assessment reports, flexibility in drivers’ working hours’ schedule, providing training on drivers fatigue management, and evaluating drivers’ health.

Given the rapid adoption of ridesourcing services (RS), it is critical for transportation planners and policymakers to understand their impacts and keep policies up to date. This study contributes to the literature by using representative samples captured in the 2001, 2009 and 2017 National Household Travel Surveys to explore how taxis and ridesourcing (T/R) services have evolved and shaped people’s travel behavior pre- and post-disruption at the US national level. It characterizes and visualizes the asymmetries in demand spatially and temporally for T/R trips, showing that ridesourcing has greatly increased T/R trips from flexible and optional activity locations to home, which vary by times of day. It also characterizes tours involving T/R services, showing that while simple optional tours (such as home–recreation–home) represent the largest share of tours involving T/R, the fastest growth has been in simple mandatory tours (such as home–work–home). Tours involving T/R grew from 0.4% of all tours in 2009 to 1% of all tours in 2017, mostly within densely populated and transit-oriented regions. Although less than 1% of T/R trips involved a direct transfer to or from transit, one-third of all tours containing T/R also included transit. However, at the same time, 40% of T/R-containing tours also involved auto trip(s). Overall, this study reveals the complex relationships among their underlying sociodemographic characteristics, RS adoption and usage behavior, and daily tour patterns.

Introduction: Traffic safety issues associated with taxis are important because the frequency of taxi crashes is significantly higher than that of other vehicle types. The purpose of this study is to derive safety implications to be used for developing policies to enhance taxi safety based on analyzing intrinsic characteristics underlying the cause of traffic accidents. Method: An in-depth questionnaire survey was conducted to collect a set of useful data representing the intrinsic characteristics. A total of 781 corporate taxi drivers participated in the survey in Korea. The proposed analysis methodology consists of two-stage data mining techniques, including a random forest method, with data that represents the working condition and welfare environment of taxi drivers. In the first stage , the drivers’ intrinsic characteristics were derived to classify four types of taxi drivers: unspecified normal, work-life balanced, overstressed, and work-oriented. Next, priority was determined for classifying high-risk taxi drivers based on factors derived from the first analysis. Results: The derived policies can be categorized into three groups: ‘the development of new policies,’ ‘the improvement of existing policies,’ and ‘the elimination of negative factors.’ Establishing a driving capability evaluation system for elderly drivers , developing mental health management programs for taxi drivers, and inspecting the taxi's internal conditions were proposed as new policies. Improving the driver's wage system, supporting the improvement of rest facilities, and supporting the installation of security devices for protecting taxi drivers are methods for improving existing policies to reinforce the traffic safety of taxi drivers. Last, restricting overtime work for taxi drivers was proposed as a policy to eliminate negative factors for improving taxi traffic safety. Practical Applications: It is expected that by devising effective policies using the policy implications suggested in this study, taxi traffic accidents can be prevented and the quality of life of taxi drivers can be improved.

Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.

This paper is the first to empirically compare the impact of fuel price on ridership of taxicabs and transportation network companies (TNCs) like Uber and Lyft. We build a theoretical model of the car service market to demonstrate how drivers under the two systems may have different reactions to fuel prices. Although all drivers pay for their own gasoline, TNC drivers have more flexibility in reducing their supply when operating costs are relatively higher. Due to greater regulation, taxi drivers are more fixed in their supply but receive a “rigidity dividend” from paying greater gas costs while profiting from the reduced competition when TNC drivers leave the market. Through ordinary least squares and seemingly unrelated regression estimation, we find that a 1-day 1% increase of fuel prices in New York City is associated with a 0.367% to 0.486% decrease in trips from TNCs, while the quantity of taxi trips will slightly increase by 0.033% to 0.088%. Empirical results additionally show a diminishing marginal effect for the fuel price elasticity of TNC trips.

With the transformation to smarter cities and the development of technologies, a large amount of data is collected from sensors in real time. Services provided by ride-sharing systems such as taxis, mobility-on-demand autonomous vehicles, and bike sharing systems are popular. This paradigm provides opportunities for improving transportation systems’ performance by allocating ride-sharing vehicles toward predicted demand proactively. However, how to deal with uncertainties in the predicted demand probability distribution for improving the average system performance is still a challenging and unsolved task. Considering this problem, in this work, we develop a data-driven distributionally robust vehicle balancing method to minimize the worst-case expected cost. We design efficient algorithms for constructing uncertainty sets of demand probability distributions for different prediction methods and leverage a quad-tree dynamic region partition method for better capturing the dynamic spatial-temporal properties of the uncertain demand. We then derive an equivalent computationally tractable form for numerically solving the distributionally robust problem. We evaluate the performance of the data-driven vehicle balancing algorithm under different demand prediction and region partition methods based on four years of taxi trip data for New York City (NYC). We show that the average total idle driving distance is reduced by 30% with the distributionally robust vehicle balancing method using quad-tree dynamic region partitions, compared with vehicle balancing methods based on static region partitions without considering demand uncertainties. This is about a 60-million-mile or a 8-million-dollar cost reduction annually in NYC.

Severe traffic congestion has promoted the development of the Intelligent Transportation System (ITS). Accurately analyzing and predicting the traffic states of the urban road networks has important theoretical significance and practical value for improving traffic efficiency and formulating ITS scheme according to local conditions. This study aims to identify and predict the traffic operation status in the road network within the Third Ring Road in Xi’an and explore spatiotemporal patterns of traffic congestion. In this paper, firstly, we discriminated the traffic status of the urban road network used the GPS data of floating vehicles (e.g., taxis and buses) in Xi’an by the Travel Time Index (TTI). Secondly, we used the emerging hot spot analysis method to locate different hot spot patterns. The time series clustering method was used to divide the whole road network’s locations into distinct clusters with similar spatiotemporal characteristics. Thirdly, we applied three different time series forecasting models, including Curve Fit Forecast (CFF), Exponential Smoothing Forecast (ESF), Forest-based Forecast (FBF), to predict the traffic operation status. Finally, we summarized the spatiotemporal characteristics of the whole-network congestion. The results of this study can contribute some helpful insights for alleviating traffic congestion. For instance, it is essential to speed up the construction of urban traffic microcirculation and increase the road network density. Moreover, it is crucial to adhere to the urban public transport priority development strategy and increase public transportation travel sharing.

Taxis offer round-the-clock, comfortable, and direct transportation services. In many cities, a large number (or all) of taxi drivers are rentee-drivers who rent taxis from taxi owners. Therefore, interaction exists between taxi owners and rentee-drivers. To regulate taxi markets effectively, it is necessary to model such interaction and investigate how different regulatory regimes affect stakeholders in taxi markets (e.g., taxi owners, rentee-drivers, and customers) and system performance (social welfare) in the presence of such an interaction. This study extends the classical aggregate taxi model to describe the supply of rentee-drivers and their interaction with a monopsony taxi owner. A general supply function is proposed to describe the supply of rentee-drivers in the market. A profit-maximization model and a social welfare maximization model are proposed to describe the decision-making process of the monopsonist and the government, respectively. Analytical and numerical studies are given to illustrate the properties of the proposed models and investigate the effects of fare adjustments and taxi rent/fleet size regulations on the monopsonist, rentee-drivers, customers, and system performance, thereby providing insights into taxi market regulations.

Road transportation accounts for significant percentages of urban energy consumption and carbon emissions. Therefore, it is important to predict and analyze the fuel consumption and emissions for on-road vehicles, which are varied under different conditions. Previous studies have shown that some traffic elements such as road type and weather condition have considerable influence on transportation fuel consumption and emissions. However, limited to the data availability, most of the existing studies focus on specific routes or scenarios, and few of them consider the effects of road type and weather condition systematically at large scale. In this research, a data-driven mesoscopic model was developed to investigate the effects of road type and weather condition on the link-level fuel consumption and emission factors based on big traffic data. This built model utilized the neural network for the prediction algorithm with inputs including road type, weather condition, and link-level aggregated operation data obtained through link-based segregation over trajectory snippets. The investigation was carried out with real-world big traffic data collected from 10,944 taxis over a 2-month period of operation in Shenzhen, and produced reliable predictions for four road types with clear and rainy weather conditions. Both statistical analysis and model prediction results showed that fuel consumption and emission factors are lower in low-speed range for freeway and expressway, and are lower in middle-speed range for main road and secondary road. In addition, rainy weather condition tends to have lower fuel consumption and emission factors than clear weather condition.

This paper presents the optimization problem of three different on-demand transit systems operated by vehicles of different sizes. This problem is aimed at minimizing the total cost of the system, which consists of the temporal cost experienced by users and the operating cost incurred by transit agencies. A compact set of estimations of the user performance and operating cost is provided, based on geometric probability. The optimization procedure allows the cost comparison of different semi-flexible services. Transit systems operated by cars (shared taxicabs) with flexible layouts are preferable for low demand densities (less than 92 pax/km2-h). For very high demand (higher than 200 pax/km2-h), bus systems with fixed layout and variable stop locations present the lowest total cost per passenger. In an intermediate domain, taxi and semi-flexible services compete among each other. The estimation of unit operating costs allows decision-makers to calculate the subsidies needed to make the system profitable. The first and last authors thank AMTU (public entity Association of Municipalities with Urban Transportation) that supported this research under the contract C-11523 “Flexitransport” with our University Barcelona TECH. The contribution of the third author has been supported by the Chilean Scientific Research Agency (CONICYT), under the grant PFCHA/BCH No. 72160291. This work was supported by the Chilean Scientific Research Agency (CONICYT), [PFCHA/BCH No. 72160291]; AMTU [C-11523].

Over the last two decades, motorcycle and motorised three-wheeler taxis have become important means of transport in many sub-Saharan African countries, including in rural areas. However, the emergi...

Examining the travel time variability (TTV) of buses, passenger cars and taxis is essential to obtain reliable travel time in urban daily trips. TTV analyses of three travel modes are conducted using travel time data collected on two urban arterial roads in Xi'an City. Firstly, the TTV is evaluated using statistical indexes. The results reveal that the TTV differs from vehicle to vehicle, period to period and site to site. Secondly, the finite mixture survival model is proposed to address the heterogeneity of travel time data by decomposing the population into several sub-populations. Wasserstein distance and Kolmogorov–Smirnov test are used to further compare the sub-populations of different vehicle types during different periods on different roads. Finally, based on the model analysis, it can be found that the finite mixture survival model is an accurate tool to examine the variability by capturing the heterogeneity of travel time data. The difference among the sub-populations suggests different travel behaviours. It concludes that more diverse travel behaviours result in higher TTV. An accurate investigation on TTV is valuable for travellers’ mode choices and transportation management agencies to obtain reliable travel time information and improve traffic efficiency.

We present a natural field experiment designed to measure other-regarding preferences in the market for taxis. We employed testers of varying ethnicity to take a number of predetermined taxi journeys. In each case, we endowed them with only 80% of the expected fare. Testers revealed the amount they could afford to pay to the driver midjourney and asked for a portion of the journey for free. In a 2 × 2 between-subjects design, we vary the length of the journey and whether a business card is elicited. We find that (1) the majority of drivers give at least part of the journey for free, (2) giving is proportional to the length of the journey, and (3) 27% of drivers complete the journey. Evidence of outgroup negativity against black testers is also reported. In order to link our empirical analysis to behavioral theory, we estimate the parameters of a number of utility functions. The data and the structural analysis lend support to the quantitative predictions of experiments that measure other-regarding preferences, and they shed further light on how discrimination can manifest itself within our preferences. This paper was accepted by Yan Chen, behavioral economics.

A considerable number of empirical studies have explored the effects of information & communication technologies (ICT) on travel in recent years. In particular, the most attention has been paid to whether the use of ICT increases or decreases trip frequency (i.e., substitution or complementarity effects). However, the subject of whether or how travel distance and mode choice are altered by ICT (i.e., modification effects) has almost been ignored. Against this background, using data collected in Beijing, China, this paper aims to explore how purchasing intangible services (e.g., eating out at restaurants, hairdressing, and visits to zoos and movie theatres) online alters the distance and mode choice of the travel to consume these services. The results suggest that due to online purchases of intangible services, people tend to travel farther to consume these services. Consequently, 25.4% of online buyers change their travel mode choices from walking or cycling (i.e., nonmotorized modes) to public transit, private cars, or taxis (i.e., motorized modes). These findings confirm the existence of modification effects of ICT on travel. Additionally, a stepwise multinomial logistic regression model and a stepwise binomial logistic regression model are used to detect the factors influencing changes in travel distance and mode choices, respectively. The regression outcomes suggest that people who have lower living costs or feel more satisfied with online purchases are more likely to increase their travel distances and to change from nonmotorized modes to motorized modes.

Taxis provide a personalized, door-to-door, and demand-responsive service, and are an attractive transport mode for accommodating the needs of an aging population. Accessible taxis further enhance the level of comfort and accessibility by providing a larger compartment and a mechanical ramp to facilitate the boarding and alighting of wheelchairs. At present, however, both ordinary and accessible taxis are infrequently used by elderly passengers in Hong Kong, due in part to high fares and long walking and wait times. Because of these limitations, taxis cannot effectively cater for the travel demands of the elderly and hence cannot improve their mobility. In this study, 580 residents of Hong Kong aged 60 or above were interviewed regarding their decision to whether to make a trip by an ordinary/accessible taxi to attend a non-compulsory social activity in hypothetical scenarios. A total of 2320 observations were obtained and a series of binary logistic regression models were calibrated to identify the significant factors influencing the elderly's willingness-to-travel. The model results show that the older elderly and those with a lower monthly expenditure were more reluctant to travel by taxi and preferred staying home. The elderly using crutches or a wheelchair had a stronger preference for accessible taxis rather than the non-walking aid users. The findings also indicate that the elderly were more sensitive to a taxi fare subsidy based on a percentage discount than one based on a fixed discount. Policy measures related to providing an accessible taxi service and taxi fare subsidy scheme to improve the mobility of the elderly are discussed: A step-wise taxi fare subsidy scheme is recommended to integrate with current transport policy measures to look after the needs of the elderly at different ages, and a supply-side subsidy is also recommended to support local taxi operators to purchase accessible taxis and increase the fleet size to minimize the wait time of users.

Air taxis are currently being demonstrated. Few studies have quantified their external effects in reducing on-road vehicle fuel consumption. The hypothesis of this paper is that air taxis may divert some drivers away from congested traffic corridors, improve traffic speed and fuel economy, and reduce congestion-induced energy consumption. A model is developed that links several key components: mode choice, the relationship between travel demand and traffic speeds, the relationship between traffic speeds and fuel economies, and the heterogenous value of travel time. It is applied to the route from downtown Los Angeles to Los Angeles International Airport, where at peak hours 38,200 vehicles attempt to use the route that has an hourly capacity of 17,200 vehicles. The model estimates that, with conservative assumptions and near-term technologies, diverting 3.2% of the traffic to air taxis could produce a 15% reduction in traffic vehicle fuel use. With optimistic assumptions and mature technologies, the study estimates that diverting 20% of traffic could reduce the traffic vehicle fuel use by about 74%. The key insight is that if a small share of congested travelers switched to air taxis, motivated by private benefits of time savings, significant external benefits for other road travelers (time savings and fuel savings) and to society (reduced energy use and emissions), would ensue creating a win-win-win outcome. These estimates (which are not intended as predictions because of the stated limitations) strongly suggest the need to consider the external energy effect in future cost-benefit analyses of air taxi technologies.

Despite the dramatic increase in online motorcycle taxis (OMTs) in many developing countries, empirical studies on passenger satisfaction toward OMTs have been overlooked. This study proposes a model of OMT passenger satisfaction where service quality and perceived value are determinants and behavioral intentions are the consequences, compared across three passenger segments based on service usage: light users, medium users, and heavy users. This research surveyed 500 OMT service users from Bandung city, Indonesia. The service quality factor analysis reveals three dimensions comprising people, physical, and application. The proposed satisfaction model is assessed by utilizing variance-based structural equation modelling, partial least squares. Examining the overall sample, this study confirms the association between service quality, perceived value, passenger satisfaction, and passenger’ behavioral intention. Further, the results of this study disclose that the relationships between these constructs differ across service usage segments. The identified relationships highlight the crucial role the factor “people” plays in determining passenger satisfaction towards OMT services, especially for heavy users. The implications of these findings, for both theoretical development and managerial practices, were examined.

This paper proposes a matching degree to study dynamic spatiotemporal characteristics of urban taxi and offers a novel understanding of self-organization taxi dispatch in hotspots on top of the Fermi learning model. The proposed matching degree can not only reflect the overall spatiotemporal characteristics of urban taxi supply and demand but also show that the density of distribution and the distance between the taxis supply and the city center will affect the satisfaction of demand. Besides, it is interesting to note that supply always exceeds demand and they will self-organize into an equilibrium state in hotspots. To understand the phenomenon, we develop the Fermi learning model based on the prospect theory and compared the results with the popular reinforcement learning model. The results demonstrate that both models can account for self-organization behavior under different scenarios. We believe our work is crucial to explore taxis data and our indicator can provide a significant suggestion for urban taxis development.

Passenger vehicles are a major source of air pollution, exposure to which increases respiratory disease risk, amplifies life-threatening conditions and burdens the public purse. The negative externalities associated with these vehicles rise further when road accidents are considered. Almost all such accidents involving fatalities transpire when private users are in single vehicle incidents or collide with each other. Though autonomous vehicle technology can mitigate these effects, widespread adoption and proliferation demands cost competitiveness with the status quo; namely, personally owned and operated, conventional vehicles. Here we show that this prospect may - in a commercially owned and operated enterprise – be unlikely. Causal factors of relevance include low capacity utilization rates and impracticable profit expectations. In a single ridership ‘autonomous taxi’ model, we find capacity utilization rates would need to improve from 52 percent to 100 percent and profits lowered by 37 percent (from 27 cents to 17 cents on a per-mile basis) for autonomous taxis to offer fares that are comparable with personally owned, conventional vehicles. In a multiple ridership model, the affordance of these fares requires a 30 percent increase in vehicle occupancy (from 1.67 to 2.2) and a 75 percent increase (1.67 to 2.92) were even lower fares offered to incentivize shared, autonomous taxi use over personally owned, conventional vehicles. We conclude that consideration of the opportunity costs of driving are integral to the widespread adoption of a technology that may dramatically improve public health outcomes.

The spatio-temporal variation in the demand for transportation, particularly taxis, in the highly dynamic urban space of a metropolis such as New York City is impacted by various factors such as commuting, weather, road work and closures, disruptions in transit services, etc. This study endeavors to explain the user demand for taxis through space and time by proposing a generalized spatio-temporal autoregressive (STAR) model. It deals with the high dimensionality of the model by proposing the use of LASSO-type penalized methods for tackling parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and the proposed models are found to outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and is suitable for practical application by taxi operators. The efficiency of the proposed model also helps with model estimation in real-time applications.

Research on attitudes towards autonomous vehicles (AVs) shows variation across gender, age, and socio-economic factors. While previous research has emphasized specific features and qualities of AVs, little is known about how attitudinal factors shape AV acceptance across a range of AV “modes” from privately-owned AVs to AV taxis shared with strangers. With an online panel of 834 US-based participants, we examine attitudes towards AVs and sharing. An exploratory factor analysis establishes four attitudinal dimensions: technology acceptance, risk-taking, traffic regulation, and driving enjoyment. We estimate multinomial logistic regression models to examine the impact of these four factors on attitudes toward AVs, willingness to purchase AVs, willingness to use AVs as a taxi service, and willingness to share AV taxis with strangers. We find a complex relationship between psychological factors and AV attitudes. “Early adopters” of technology and those who support stricter traffic regulations are more likely to have a positive attitude about AVs, whereas those who avoid risky behavior were more likely to have a negative attitude instead of a neutral attitude. Similar patterns were found across models of purchasing, using, and sharing AVs. The results imply that people who support traffic regulations may perceive AVs as a safer transport mode than human-driven cars, while those who avoid risk-taking behavior may perceive AVs to be more dangerous. However, we find that a large fraction of the population is not yet ready to use an AV with no driver, and overall reluctance to share a ride in an AV taxi.

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 objective of this paper is to highlight important differences between taxis and transportation network companies (TNCs) in a large urban area. We analyze the publicly available dataset from Chicago which includes taxi and transportation network company (TNC) utilization and the level of service measures from five months in 2013–2014 and the same five months in 2018–2019. We compare and contrast the data from these two points in time to document utilization of taxis and TNCs and to measure differences in travel times, travel distances, fares, destinations served, and the spatial and temporal distribution of these trips. Travel to and from airports has been evaluated separately owing to the exceptionally high number of trips they generate. Striking differences between pooled and unpooled TNC trip volumes and other travel metrics have been assessed to highlight their operational diversity despite being considered as the same mode. The exploratory analysis has been carried out across the shared-ride, time, and mode dimensions. The study revealed both similarities and differences in taxi trip characteristics between the two evaluation periods and also outlined how the ridehailing market has grown over the years despite the near stagnation in population and employment in the city. We believe that assessing how taxis have fared through this time and highlighting the intrinsic differences between how the old and new mode of on-demand ride services coexist is important. This study aims to help understand how new-age mobility services are impacting transportation in one of the largest cities in the U.S.

Due to the ever-growing concerns over air pollution and energy security, more and more cities have started to replace their conventional taxi fleets with electric ones. Even though environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures, and inadequate taxi supply due to the long charging time. In this article, we conduct the first longitudinal measurement study to understand the long-term evolution of mobility and charging patterns by utilizing 5-year data from one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, (1) we first perform an electric taxi contextualization about their operation and charging activities; (2) then we design a generic charging event extraction algorithm based on GPS data and charging station data, and (3) based on the contextualization and extracted charging activities, we perform a comprehensive measurement study called ePat to explore the evolution of the electric taxi network from the mobility and charging perspectives. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolution process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolution process. Our ePat as a comprehensive measurement of the electric taxi network mobility and charging evolution has the potential to advance the understanding of the evolution patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

This study sought to assess rural people‘s level of satisfaction with motorcycle taxi transport services, taking Rongo Sub-County, Kenya as a typical case. This awareness is crucial to transport planning, particularly in the pursuit of an equitable mobility system in rural Kenya, Africa and beyond. The main data collection tool was a semi-structured questionnaire which was complemented by a key informant interview schedule, focus group discussions guide and an observation checklist. The study found that while the satisfaction of the respondents ranged from neutral to satisfied there was a strong difference between groups. Most of the respondents were satisfied with the MT services, largely due to the physical attributes of the MT. Negative reasons were mainly related to MT riders‘ mannerism, unprofessional driving, and poor safety. Nonetheless, almost all respondents recognised the importance of motorcycle taxi transport in realising their needs of accessing locations and activities within and outside Rongo Sub-County. This paper strongly recommends that rural transport needs and options should be understood from the point of view of rural people (differentiated by age, gender, occupation and income) in order to provide better rural transport services that meet different needs.

The rise of e-hailing taxis has significantly altered urban transportation system and led to a competitive taxi market with both traditional street-hailing and e-hailing taxis. The various taxi services provide similar door-to-door rides, thus compete with each other. Meanwhile, the expanding fleet size of e-hailing taxis, together with considerable number of traditional taxicabs, influence the urban road network performance, which can also in turn affect taxi mode choice and operation. In this study, we propose an innovative modeling structure for the competitive taxi market and capture the interactions not only within the taxi market but also between the taxi market and urban road system. The model is built on a network linking a certain number of queues, derived from both the taxi and urban road system. Considering both the passenger and vehicle arrivals, we utilize an assembly-like queue SM / M / 1 for passenger-vehicle matching within the taxi system, which controls how many and how frequently vehicles drive from the taxi system to the urban road system. A common multi-server M / M / c queue that can account for road capacity is proposed for the urban road system and a feedback of network states is sent back to the taxi system. Then the stationary state distributions, as well as asymptotic properties, of the queueing network are discussed. An example is designed based on data from New York City. Numerical results show that the proposed modeling structure, together with the corresponding approximation method, can capture dynamics within high demand areas and, in particular, a spatially balanced system. Overall, this study shows how the queueing network approach can measure aggregated performance of various taxi services, for instance, not only the waiting/searching time during passenger-vehicle matching but also the delays in the urban road network. Furthermore, the model can be generalized to study the control and management of taxi markets.

This paper develops cost models for urban transport infrastructure options in situations where motorcycles and various forms of taxis are important modes of transport. The total social costs (TSCs) of conventional bus, Bus Rapid Transit (BRT), Monorail, Metro (Elevated Rail), car, motorcycle, Taxi and Uber are calculated for an urban corridor covering operator, user and external costs. Based on the parameters for a 7 km corridor in Hanoi, Vietnam, the results show the lowest average social cost (ASC) transport modes for different ranges of demand. Motorcycle might be the best option at low demand levels while conventional bus has advantages with low-medium demand. At medium demand levels, bus-based technologies and Monorail are competitive options while Metro, with a higher person capacity, is the best alternative at the highest demand levels. Compared to other modes, the ASCs of car and Taxi/Uber are greater because of high capital cost (related to vehicles) per passenger and low occupancy. Transport planners and decision makers in low and middle income countries (LMICs) can draw on the findings of this study. However, various limitations are identified and additional research is suggested.

With the rapid increase of vehicles in use worldwide, the need for efficient traffic monitoring systems has arisen. This work proposes a low-cost vehicular traffic monitoring system using IoT devices and fog computing. The system is based on a three-tiered architecture which is composed of (i) the mobile tracking system that records the positions of the vehicles using GPS technologies, (ii) the information gathering system which gathers all the data collected by the mobile tracking system, and (iii) the fog devices that process the data collected and extract the information needed. The system is tested in the town of Corfu during a period of increased tourism when the traffic is considered to be relatively dense. The mobile tracking system devices are placed on taxis and with the help of professional taxi drivers the accuracy of the data collected is evaluated. The system is able to record the movement of the vehicles accurately using its own independent data. The results can be remotely accessed by utilizing fog and cloud computing infrastructure established to process the data and upload it on a server. The system is used to give a better understanding of the speed variance in the center of the town during different dates and hours. In conclusion the system presented in this study can be utilized to monitor the traffic and provide vital information about its behavior in relation to time.

For decades, taxis have provided for-hire vehicle service without significant competition from other modes. But in 2012, ride-hail services such as Uber and Lyft upended the taxi business model by connecting riders to drivers through smartphone applications. Since then, few studies have considered how taxis compare to their new competition or how service quality varies across the two modes. We use data from 1680 ride-hail and taxi trips taken in a Los Angeles audit study to ask: how do service qualities—including trip price, reliability, and accountability—vary across ride-hailing (Uber and Lyft) and taxis? We find that, for the same origin and destination pair, ridehail users pay 40% lower fares and wait about one-quarter of the time compared to taxis. In addition, ride-hailing nearly guaranteed a ride, while about one in five taxi riders were never picked up. We utilize semi-structured interviews of frequent taxi and ride-hail users to examine potential explanations of the stark quantitative differences observed across modes. Findings reveal that, despite technological disruption, travelers continue to prioritize the long-standing tenets of transportation services: affordability, reliability, and accountability. Ride-hailing successfully implemented technologies to deliver on these traveler priorities, while taxis have largely failed to capitalize on new technologies. Findings suggest that taxis will need to do more than lower prices or put more cars on the road if they hope to compete with ride-hailing services, and that both new and old modes should harness technology to deliver on long-standing transportation service goals.

This paper presents a model to predict the market penetration of autonomous cars for passenger transportation, focussing on autonomous taxis without a steering wheel. For this, a discrete system dynamics model was created and evaluated where the input parameters have been taken from the literature and a survey. In this survey, user demands were investigated among 873 participants. It showed that gender, age, job situation, city size and monthly income have an impact on the trust in the technology of autonomous driving as well as on the willingness to use autonomous taxis. In contrast, no evidence of an impact was found for the respondent’s education or the numbers of adults or children in the household. The results indicate that the majority of autonomous driving vehicles will be private and will have a steering wheel within the scope of this study, which covers the time frame until 2040. These are expected to reach 12.4 million units. At the same time, autonomous taxis will enter the market. These are expected to be mainly non-controllable (without a steering wheel), reaching a maximum of 2.4 million units by 2038. This maximum mainly depends on the trust in the technology as well as on people’s willingness to give up their own car due to cost or usage factors. As an autonomous taxi supports more people on average, the total number of vehicles is expected to drop from 45.1 million to around 41.7 million by 2040. Furthermore, the percentage of people travelling primarily by taxis and public transport is expected to increase from today’s 20.0% to about 32% in 2040. Then, about 19% will use autonomous taxis at least occasionally. Lastly, the vehicle miles travelled are expected to increase by 25% with people switching from public transport to autonomous taxis.

In today's world, massive on-demand mobility requests are dispatched every hour on ride-sourcing platforms, however, customers later cancel quite a number of these confirmed orders. This paper makes the first attempt to look into this customer confirmed-order cancellation behaviour based on a two-month, hourly average dataset of Didi Express in Shanghai provided by Didi Chuxing. The mean ride distance and pick-up distance of cancelled orders are observed to be obviously longer than those of completed orders of the same time period, reflecting an obvious impact of travel cost on customer decisions of order cancellation. However, the correlation between the mean customer confirmed-order cancellation rate (COCR) and the mean customer waiting time for pick-up of cancelled orders is significantly negative. Shanghai, like many other cities around the world, has coupled ride-sourcing and taxi markets, and as such, this counter-intuitive phenomenon can be explained as an outcome of the lower (higher) chance of meeting vacant taxis while waiting for pick-up during peak (non-peak) hours. We formulate COCR as a function of customer waiting time for ride-sourcing vehicles and cruising taxis, the penalty strategy by the platform for cancellation of confirmed orders, and customers’ own characteristics (i.e., ride distance, value of time, perceived psychological cost of order cancellation, additional safety concern over ride-sourcing), and propose a system of nonlinear equations to depict the complex interactions between the ride-sourcing and taxi markets considering the probability of ride-sourcing cancellation after order confirmation. With the proposed model, we replicate the observed lower COCR under a higher demand rate and longer waiting time for pick-up through numerical examples, and highlight the potential improvement of platform profit that can be achieved by appropriately designed penalty/compensation strategies.

This paper discusses a policy enacted in 2016 by the city of Florence mandating the use of electric vehicles for the taxi service. We interviewed the taxi license owners (carriers), their co-operatives and the city administrators to evaluate the policy implementation, its acceptability and the cost-competitiveness of electric taxis (e-taxis) with respect to their hybrid and diesel alternatives. Using the data collected during the interviews, we estimated the total cost of ownership (TCO) of e-taxis and their conventional counterparts for the models comprising the Florence taxi fleet. We find that e-taxis already represent a valid alternative to the conventional ones not only from an environmental point of view but also from a private economic one. In particular, when the Florence-specific discounted purchase prices are considered and the base case scenario applies, e-taxis are the cheapest choice in terms of TCO and no financial incentives are needed. When the manufacturer suggested retail prices are considered, e-taxis are slightly less cost-competitive than hybrid taxis but more cost-competitive than the diesel ones. Sensitivity analyses are performed to evaluate the impact of the annual distance travelled, Ecobonus purchase subsidy, net revenue loss, annual percentage rate charged to finance the taxi acquisition and insurance premium.

The combination of self-driving technology and taxis has a considerable potential in improving service quality and economic efficiency that can completely change traditional taxi operation. This study accordingly focuses on the autonomous taxi (aTaxi) dispatching problem in hybrid request mode where travelers can either request immediate rides or reserve taxi services ahead of time. In this study, a centralized dispatcher and decentralized autonomous dispatchers are designed to plan short-term and long-term routes for aTaxis in real time, respectively. The centralized dispatcher integrates vehicle-to-passenger assignment with empty vehicle rebalance to guarantee solution quality. Decentralized autonomous dispatchers distribute a portion of the calculation to aTaxis, thus reducing the centralized dispatcher’s workload. The dispatching strategy ensures that the response to traveler requests can be made immediately, and if such requests are accepted, the travelers will receive the expected services. Finally, experiments are conducted based on the actual road network and the trip data of Manhattan to investigate the dispatching strategy. The results confirm the high performance of the proposed dispatching strategies in terms of service quality and economic efficiency. It is also found that the increase in reservation rates can enhance the robustness of the method with respect to errors in predicted requests. Moreover, when the number of vehicles is adequate and reservations are made longer ahead of time, the completion rate of requests and the revenue improve.

With the advent of GPS tracking technology, how to make use of taxi trajectories to efficiently and effectively reduce taxis cruising distance is an active and challenging research topic. In this paper, we propose a profitable taxi route recommendation method called adaptive shortest expected cruising route (ASER). In ASER, a probabilistic network model is developed to predict pick-up probability and capacity of each location by using Kalman filtering method. To recommend profitable driving routes to taxi drivers, ASER takes the load balance between passengers and taxis into consideration and the shortest expected cruising distance is introduced to formulate potential cruising distance of taxis. Moreover, MapReduce and a novel data structure kdS-tree are applied to improve recommendation efficiency. ASER is evaluated on two real trajectory datasets from San Francisco, CA, USA, and Wuhan, China. The experimental results validate that ASER significantly outperforms the existing methods by reducing the taxi cruising distance 11% and 39%.

Taxis are an important transportation mode in many cities due to their convenience and accessibility. In the taxi-dispatching problem, sometimes it is more beneficial for the supplier if taxis cruise in the network after serving the first request to pick up the next passenger, while sometimes it is better that they wait in stations for new trip requests. In this article, we propose a rolling horizon scheme that dynamically optimizes taxi dispatching considering the actual traffic conditions. To optimize passenger satisfaction, we define a limitation for passenger waiting time. To be able to apply the method to large-scale networks, we introduce a clustering-based technique that can significantly improve the computation time without harming the solution quality. Finally, we test our method on a real test case considering taxi requests with personal car trips to reproduce actual network loading and unloading congestion during peak hours.

In high-capacity ridesharing, increasing the number of passengers per shared vehicle can achieve long-term profitability for the public transport providers; hence, the ridesharing success rate (i.e., the percentage of successful applicants) is an important indicator of monetary cost. Unlike peer-to-peer ridesharing vehicles, to open a route, high-capacity ridesharing vehicles need to satisfy the minimum loadable capacity. However, an insufficient amount of similar travel demands gathered on less popular routes can result in the forced cancellation of many bus lines, thereby reducing the ridesharing success rate. In this paper, for a bus ridesharing service, we present an optimization approach for maximizing the ridesharing success rate. Utilizing this approach, we solve the problem of low vehicle utilization by using a dynamic grid-based heuristic algorithm with performance guarantees and a novel bus ridesharing model of a hybrid form of ridesharing that combines slugging with hitchhiking. To further assess the effectiveness of the proposed approach, we evaluate the solution by using benchmarks from a real-life dataset that contains 65,065-trip instances extracted from 10,585 Shanghai taxis from one day (April 1, 2018), and we compare our results with solutions provided by the existing forms of ridesharing. The experimental results demonstrate that the proposed approach can improve the ridesharing success rate by 79.6%.

Taxis provide essential transport services in urban areas. In the taxi industry, the income level remains a cause of concern for taxi drivers as well as regulators. Analyzing the variation trend of taxi operation efficiency indicators throughout the day, mining high-income orders hot-spots and high-income regions at different periods, will effectively improve the average hourly incomes (AHI) of drivers. This paper selects the order data for each day of holidays, working days, and non-working days through the taxi order dataset of October 2019 in Xi’an. Firstly, we analyze the variation trend of taxi operation efficiency indicators in the three days. We next divide the orders into four income levels based on the Natural Breaks accordingly. Then, we use Tyson polygon and mash map matching methods to visualize the high-income orders hot-spots and high-income regions. It is significantly to analyze and summarize the visualization results. Finally, we compute the Moran’I index to measure the spatial correlation between high-income orders regions and high-income regions. The results show that (1) the number and the spatial distribution of high-income orders hot-spots and high-income regions at different periods are different. (2) Some places are hot-spots, but neither high-income orders hot-spots nor high-income regions. (3) The high-income orders regions and high-income regions have a strong correlation in spatial distribution. This study provides suggestions and insights to taxi companies and taxi drivers to increase their average hourly income (AHI) and enhance the efficiency of the taxi industry.

Taxis are an indispensable part of the public transportation system; however, the industry is confronting many challenges, such as the development of mobile apps for reservations and the increase in large-scale ridesharing. Taxi demand is impacted by many spatial and temporal factors, such as the location, the built environment, the time of day. This study analyzes various factors associated with temporal and spatial densities of taxi pick-up locations using a generalized additive mixed model. The key findings are as follows: (1) taxi demand is higher in densely developed areas, which are characterized by high degrees of mixed land use, high population densities, dense road junctions, and high percentages of residential, commercial and public space; (2) taxi demand is higher in areas with denser secondary roads; (3) taxi demand is lower in areas with more bus stops; (4) taxi demand is higher during weekdays, peak hours, and warmer days (autumn season); and (5) despite taxi demand being positively associated with the rainy season, this relationship has strong nonlinearity. To optimize the efficiency of taxi system, there should be more loading spaces for taxis in activity-concentrated areas, which are characterized by a higher population density, high levels of mixed land use and dense road junctions. In addition, taxi drivers should search for passengers in underserved areas to reach potential passengers during nonpeak hours. Future research may use data recorded via mobile apps to examine taxi overtime waiting, rejection of service and prepaid empty trips to provide a complete image of taxi demand.

Excessive competition between taxis and subways has eroded the advantages of public transit systems such as worsening road traffic congestion and environment. This study aims to improve the appeal of subways by a comprehensive understating of competition between taxis and subways. We investigate competitive relationship between these two transportation modes by using empirical multi-source data. First, non-negative matrix factorization (NMF) algorithm is used to discover the spatiotemporal travel patterns of subway-competing taxi users (SCTUs). Second, we propose a new index to quantify the competitiveness of subways based on the actual mode choices results. Then, we reveal the spatiotemporal heterogeneity of competitiveness from perspective of subway network. Taking Beijing, China, for a case study, we extract a week's worth of GPS records on taxi trajectory and smartcard data of subways. Subway-competing taxi trips (SCTTs) account for the largest proportion of the total taxi trips. As a result, three basic patterns are found in SCTTs. Subway station pairs with high and less competition are divided according to competitiveness index. Among low competition station pairs, three spatial structures are observed, including low-competition collinearity corridors, radial communities, and links between paralleled subway lines. Combining the distribution results of travel pattern and competitiveness degree, short-term and long-term planning suggestions are recommended respectively for station pairs with high demand but low competitiveness and those with low demand and low competitiveness. These findings provide useful insights into promoting more effective and sensitive policies to balance the competition and attract more taxi passengers to the subway system.

Aviation industry has increased its potential in the last 100 years. From the first steps whereas aviation 1.0 can be defined as the period between the first powered flight (1903) and the advent of the first commercial airliner that was operated on a scheduled international route, the Airco DH.16 (1919) between London and Paris, humanity has faced with the (r)evolution which goes by the many names of flying cars, air taxis, personal air vehicles (PAVs), electric vertical take-off and landing (eVTOLs) vehicles, and finally, Urban Air Mobility. Therefore, the concept of safety and its evolution has been changed for many times. Those changes are influenced by different approaches which roughly align with eras of activities in the aviation science and practice. Main approaches, by the time of occurrence, are: technical era (from early 1900s until late 1960s), human factors era (from early 1970s), organizational era (from mid 1990s) and total system era (from the beginning of the 21st century). Compounding evolution of technology and development in aviation industry and safety treats and aspects, this paper provides overall analysis of creating dominant safety management principles in aviation industry. Moreover, paper considers aircraft accidents caused by flight crew errors since it could be the main problem in urban air mobility safety issues. The final part is dedicated to the States role in performing aviation safety and challenges they are faced with. The purpose of the paper is to provide comprehensive platform for safety analysis within the new concept of urban air mobility.

The work is dedicated to the possibility of using hydrogen-powered vehicles in urban transport systems. Due to the need to look for alternative solutions for vehicles with conventional drive in cities, hydrogen-powered cars are one of the practical possibilities of realizing the sustainable transport assumptions and independence from oil imports - which is one of the main priorities of the European Union. This paper presents a literature analysis, the analysis of the current state and development of use hydrogen-powered vehicles in the world. The article refers to the possibilities of use hydrogen-vehicles in different ways of mobility: individual vehicles, taxis and shared mobility. In addition, the author focused on showing the advantages and disadvantages of using hydrogen-powered vehicles in urban transport systems.

Extreme weather days were usually removed as outliers in past studies of taxi ridership. However, taxis play an important role during extreme weather events when other public transportation service is suspended. Thus, estimating spatio-temporal variations of taxi ridership during extreme weather conditions can provide valuable information on heretofore relatively unknown behavior of taxi riders and help identify areas with unusual taxi demands. In this study, New York City (NYC) taxi ridership shortly before the landfall of Hurricanes Irene and Sandy was analyzed. It was found that taxi ridership began to drop about 24 h before each hurricane made landfall. Six multisource regression models were estimated to explain the variation of taxi ridership in the last 24 h. Characteristics of the approaching hurricane, local weather conditions, and zonal socio-demographic variables were entered as explanatory variables. It was found that taxi ridership during hurricane-affected periods has a strong linear association with the ridership in unaffected periods but the proportion decreases as the storm approaches; a storm has the greatest impact on taxi ridership during weekend and at night, and the least impact on a weekday during the day; and taxi users make fewer trips during conditions of heavy rain or strong wind.

Transients, such as tourists or business travelers, often fly into large cities and subsequently have to rely on public transportation for movement within the city. It is difficult to observe transient behavior in the event of a disaster through traditional survey methods. Thus, transient behavior has not been understood as well as resident behavior, which presents a challenge to emergency management. This study used taxi trip records that are passively collected year-round in New York City (NYC) to observe transient behavior during Hurricane Sandy (2012). The linkage between transients and taxi trips was built upon the fact that many transients choose hotels as accommodation and depend on taxis during their travel in NYC. This study extracted and analyzed taxi trips originating from hotels shortly before and after the hurricane landfall, which cover transient outbound activities in a week. In the pre-landfall period, it was found that transients who evacuated out of Manhattan were more likely to leave hotels about 18–24 h before hurricane landfall; and those who stayed made more shopping trips within Manhattan. In the post-landfall period, transients were more likely to relocate on the second day after hurricane landfall; some transients headed to local airports once limited service was resumed. This is believed to be the first study to use passively collected data to observe transient travel behavior in the event of a hurricane as well as on normal days.