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Acceptability, Energy Consumption, and Costs of Electric Vehicle for Ride-Hailing Drivers in Beijing
Abstract and Figures
The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 hours; these values are substantially larger than those for household drivers (40.0 km and 1.5 hours). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.
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... To the best of our knowledge, this stream of research focuses on situations in metropolises such as in New York [15], Beijing [16], and San Francisco [17]. Ride-hailing services are increasingly popular not only in metropolitan cities but also in China's small-and medium-sized cities [18]. ...
... There was a substantial body of literature dealing with ridehailing services. Previous studies have examined several aspects, ranging from the impacts of ride-hailing services on the economy [23][24][25], society [16,26] and traffic [27,28,29], as well as research on policy supervision of ride-hailing services [30,31]. In this context, a considerable volume of studies was devoted to exploring whether ride-hailing services had complementary or substitution effects, as well as mixed complementary and substitution effects on public transit usage. ...
... Rayle et al. [17] used an intercept survey in San Francisco to explore the ride-hailing role in urban transportation, finding that ride-hailing services grabbed a large part of public transit ridership. Wei et al. [16] built a mathematical model to denote Beijing citizens' travel demand and further conducted an experimental simulation to analyse the relationship between Beijing's annual passenger volume of ride-hailing services and its public transit ridership. The result showed that ride-hailing services partially substituted public transit. ...
With the recent advances in smartphones and Internet technologies, ride‐hailing services (such as Uber and Didi) have emerged and changed the travel modes that residents use. An important issue within this area is how ride‐hailing services influence public transit usage. The majority of the research regarding this topic has focused on situations in large cities and has not reached a unanimous consensus among scholars. In particular, the role of ride‐hailing services in small‐ and medium‐sized cities may be different from the role of these services in large cities. In this paper, we choose 22 small‐ and medium‐sized cities in China as samples with a research time window spanning from 2011 to 2016 to examine the impact of the introduction of ride‐hailing services on public transit usage. The results of the synthetic control method, as well as other robustness checks, show that (1) the introduction of ride‐hailing services to China's small‐ and medium‐sized cities significantly increases public transit usage; (2) the effect of the introduction of ride‐hailing services on public transit usage in small‐ and medium‐sized cities is “proactive” for approximately 1 year; and (3) the positive effect of ride‐hailing services on public transit usage in small‐ and medium‐sized cities weakens over time. This study enriches the literature on the impact of ride‐hailing services on the urban transportation system by specifically taking small‐ and medium‐sized cities as the research scope. The above findings are of great significance to the urban transport department's formulation of ride‐hailing policies and the operation layout of public transit operators in small‐ and medium‐sized cities.
... However, at present, the shortcomings of electric vehicles (EVs) are dissuading drivers and operators from adopting E-taxis. Compared with conventional internal combustion engine (ICE) vehicles, E-taxis have shorter driving ranges largely due to insufficient battery capacity (Kempton 2016, Tu et al. 2019. And limited by current charging technologies, E-taxis usually take hours to fully recharge, which is significantly longer than the time for refueling (Tu et al. 2021). ...
... Many efforts have been made to improve the viability of E-taxis in different aspects, such as locating E-taxi charging stations (Tu et al. 2016, Meng et al. 2020, scheduling E-taxi recharging events, balancing charging stations utilization, and route recommendations. Tu et al. (2019) used the GPS trajectories of ride-hailing drivers in Beijing to investigate the extent to which EVs could satisfy the demand for ride-hailing services. Their results suggest long-range vehicles and more extensive coverage of charging facilities are essential for the success of EV-based ride-hailing. ...
There is a growing interest in the optimization of vehicle fleets management in urban environments. However, limited attention has been paid to the integrated optimization of electric taxi fleets accounting for different operations as well as complex spatiotem-poral demand dynamics. To this end, this study develops a real-time recommendation framework based on deep reinforcement learning (DRL) for electric taxis (E-taxis) to improve their system performance with explicit modeling of multiple vehicle actions and varying travel demand across space and over time. Spatiotemporal patterns of urban taxi travels are extracted from large-scale taxi trajectories. Spatiotemporal strategies are proposed to coordinate E-taxis' repositioning and recharging with optimized recommendation for next destinations and charging stations. A spatiotemporal double deep Q-network (ST-DDQN) is embedded in the DRL framework to maximize the daily profit. A prototype real-time recommendation system for E-taxis is implemented for the decision-making of E-taxi drivers and sensitivity analyses are carried out. The experimental results in Shenzhen, China suggest that the proposed framework could improve the overall performance. This study will benefit the promotion of connected E-taxis and the development of clean and smart transportation. ARTICLE HISTORY
... Researchers have so far carried out many studies on zero-emission vehicles, such as analyzing the influencing factors of the energy consumption of electric vehicles and their impact degrees [19][20][21][22][23][24][25][26][27][28][29][30], analyzing and predicting the energy consumption of electric buses [31][32][33], discussing the feasibility of the electrification of taxis or ride-hailing vehicles [34,35], studying charging behavior and predicting charging demand [36,37], assessing the potential for carbon reduction over the entire life cycle [38], exploring the transition process of traditional cars to electric cars in developing countries [39], etc. However, most studies use model simulation or experimental methods, and a few studies manage to combine the actual driving data of passenger cars on actual roads [21,24,25,28,[34][35][36]. For electric heavy-duty trucks, Afsane Amiri et al. used a bi-objective programming model to study the green vehicle routing problem of electric heavy-duty trucks and traditional trucks [40]; Robert L. et al. evaluated the performance of a Class 5 battery electric urban delivery vehicle, focusing on two standardized driving cycles and employing a steadystate range test on a chassis dynamometer [41]; Mareev et al. used simulation methods to study the life cycle cost of electric trucks and the battery demand in long-distance transportation scenarios [42]; Tong Fan et al. used model simulation methods to study the charging behavior and charging load of electrified long-haul freight trucks [43]; Emir Çabukoglu et al. used survey data and data from an automated performance monitoring system of Swiss truck fleets to analyze the feasibility of replacing diesel trucks with electric heavy-duty trucks [44]; Brennan Borlaug et al. studied the charging demand of electric semi-trailer trucks based on US large-scale vehicle telematics data [37]; Burak Sen et al. used a hybrid life cycle assessment method to analyze and compare the differences in life cycle emissions, costs, and externalities between Class 8 category electric heavy-duty trucks and other alternative fuel trucks [45]; Susumu Sato et al. used a chassis dynamometer to study the energy consumption rates and energy regeneration rates of three types of electric heavy-duty trucks [46]. ...
... Researchers have so far carried out many studies on zero-emission vehicles, such as analyzing the influencing factors of the energy consumption of electric vehicles and their impact degrees [19][20][21][22][23][24][25][26][27][28][29][30], analyzing and predicting the energy consumption of electric buses [31][32][33], discussing the feasibility of the electrification of taxis or ride-hailing vehicles [34,35], studying charging behavior and predicting charging demand [36,37], assessing the potential for carbon reduction over the entire life cycle [38], exploring the transition process of traditional cars to electric cars in developing countries [39], etc. However, most studies use model simulation or experimental methods, and a few studies manage to combine the actual driving data of passenger cars on actual roads [21,24,25,28,[34][35][36]. For electric heavy-duty trucks, Afsane Amiri et al. used a bi-objective programming model to study the green vehicle routing problem of electric heavy-duty trucks and traditional trucks [40]; Robert L. et al. evaluated the performance of a Class 5 battery electric urban delivery vehicle, focusing on two standardized driving cycles and employing a steadystate range test on a chassis dynamometer [41]; Mareev et al. used simulation methods to study the life cycle cost of electric trucks and the battery demand in long-distance transportation scenarios [42]; Tong Fan et al. used model simulation methods to study the charging behavior and charging load of electrified long-haul freight trucks [43]; Emir Çabukoglu et al. used survey data and data from an automated performance monitoring system of Swiss truck fleets to analyze the feasibility of replacing diesel trucks with electric heavy-duty trucks [44]; Brennan Borlaug et al. studied the charging demand of electric semi-trailer trucks based on US large-scale vehicle telematics data [37]; Burak Sen et al. used a hybrid life cycle assessment method to analyze and compare the differences in life cycle emissions, costs, and externalities between Class 8 category electric heavy-duty trucks and other alternative fuel trucks [45]; Susumu Sato et al. used a chassis dynamometer to study the energy consumption rates and energy regeneration rates of three types of electric heavy-duty trucks [46]. ...
In order to understand the driving characteristics of electric heavy-duty trucks in practical application scenarios and promote their usage to replace diesel trucks, this study analyzed the actual usage of electric and diesel heavy-duty trucks in a steel factory based on vehicle-monitoring data and remote online monitoring data and estimated the emission reduction potential of the application of electric trucks by using a mileage-based method and the greenhouse gas emission model. The results showed that the electric heavy-duty trucks in the steel factory mostly operated for over 14 h, with a vehicle kilometers traveled (VKT) of 50–300 km each day, which could meet most of the demands of the transportation of the steel industry. The average daily energy consumption for most trucks falls within the range of 210–230 kWh/100 km, with higher consumption in winter than in summer, which can save approximately 18–26% in operating costs compared with diesel trucks. It is estimated that the usage of these electric heavy-duty trucks can achieve an annual reduction of 115.8 tons of NOx emissions, 0.7 tons of PM emissions, and 18,000 tons of CO2 emissions. To further promote the application of electric heavy-duty trucks in China, several policy suggestions, such as introducing priority road-right policies, promoting vehicle and battery leasing markets, and exempting zero-emission vehicles during heavy pollution days, were proposed.
... Combining Uber and Lyft data from New York City and San Francisco with agent-based simulations, [98] show EVs can provide the same level of ridehailing service with only three to four 50kW chargers per square mile. [99] performs a similar analysis using GPS trajectories of DiDi Chuxing drivers in Beijing. They find a 200 kilometer range EV would satisfy the needs of more than half of drivers, assuming slower home charging is fully available. ...
Ride-hailing has expanded substantially around the globe over the last decade and is likely to be an integral part of future transportation systems. We perform a systematic review of the literature on energy and environmental impacts of ride-hailing. In general, empirical papers find that ride-hailing has increased congestion, vehicle miles traveled, and emissions. However, theoretical papers overwhelmingly point to the potential for energy and emissions reductions in a future with increased electrification and pooling. Future research addressing the gap between observed and predicted impacts is warranted.
... Therefore, in the development of charging stations, a comprehensive evaluation of user charging behavior characteristics is indispensable to ensure that these stations are aligned with user requirements and enhance their utilization efficiency, thus avoiding the wasteful allocation of resources. Previous research has employed diverse data sources for investigating charging behavior and assessing potential demands, including GPS trajectory data, surveys, and vehicle registration data [7][8][9]. However, these data can only indirectly estimate charging demands, and errors may arise during the evaluation process due to variations in model parameters or conversion methods. ...
Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.
... Transportation electrification has been widely recognised as a necessary mean to improve energy efficiency and reduce emissions [1]. In the intelligent and connected environment, accurate and real-time energy consumption (EC) prediction for electric vehicles (EVs) is essential to improve the travel service experience, as well as provide support for optimising battery design [2], planning an energy-saving travel route [3] and improving charging infrastructure [4]. ...
Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.
... Meanwhile, realizing the real-world energy consumption of road EVs is the greatest challenge to effectively promoting transport's sustainable development. In Beijing, China, the real-world energy consumption and carbon dioxide (CO 2 ) emissions of EVs, including personal vehicles and taxis, were declared (Wei et al., 2019). In Chiang Mai, Thailand, the energy consumption of EVs, including ☆ 2023 8th International Conference on Sustainable and Renewable Energy Engineering (ICSREE 2023) 11-13 May, Nice, France personal vehicles (Suttakul et al., 2022a(Suttakul et al., , 2022b and public buses (Kammuang-lue and Boonjun, 2021), were reported. ...
The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policy-makers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.
Electric mobility is getting prominence in modern transportation as government policies aim to reduce greenhouse gas (GHG) emissions. In the context of real-time testing, numerical modelling and simulation of electric vehicle (EV) powertrains play a vital role in developing an efficient electric powertrain and charging infrastructure as it consumes less time and cost. Also, it enhances the overall performance by optimizing the size and configuration of the EV powertrain under different driving conditions. Thus, the review paper explores the different modelling approaches used for estimating the energy consumption (EC) and driving range (DR) initially. Further, the vehicle analytical model is discussed in detail with sub-models of powertrain components and vehicle dynamics, which have the mathematical correlation related to power losses and energy flow. Additionally, the necessity, development process, characterization and accuracy of localized driving cycles (DCs) and commonly used driver controller models for EVs are critically elaborated. Further, the impact of various influential input parameters such as vehicle parameters and driving conditions on EV performance characteristics is analyzed along with different improvisation methods utilized in the existing literature. From this extensive review, it can be concluded that simulation results by using an analytical vehicle model have good accuracy with chassis dynamometer-based testing and it can be used for optimizing the size and configuration of EV powertrain components under different scenarios. Finally, the present status and future research required in the field of EV powertrain development through modelling and simulation are summarized to extend the application of EVs in transportation sectors. INDEX TERMS Electric vehicle, modelling and simulation, analytical vehicle model, driving range, energy consumption.
This paper presents a stochastic bottom-up model to assess electric vehicles’ (EV) impact on load profiles at different parking locations as well as their potential for load management strategies. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging electricity demand and behaviour at different locations. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behaviour could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver's occupation has the strongest influence on the first departure time and parking time whilst at work. EV use is modelled using an inhomogeneous Markov-chain to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to model presence at a charger and calculate electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. Individual charging behaviour is included in the model using a logistic function accounting for the sensitivity of the driver towards (low) battery SOC. The model output is compared to the mobility dataset to test its validity and shown to have a deviation in key household mobility characteristics of just a few percentage points. The model is then employed to analyse the impact of uncontrolled charging of EV on the residential load profile. It is found that the absolute load peaks will increase by up to a factor of 8.5 depending on the loading infrastructure, the load in high load hours will increase by approx. a factor of three and annual electricity demand will approximately double.
The growth of app-based taxi services has disrupted the urban taxi market. It has seen significant demand shift between the traditional and emerging app-based taxi services. This study explores the influencing factors for determining the ridership distribution of taxi services. Considering the spatial, temporal, and modal heterogeneity, we propose a mixture modeling structure of spatial lag and simultaneous equation model. A case study is designed with 6-month trip records of two traditional taxi services and one app-based taxi service in New York City. The case study provides insights on not only the influencing factors for taxi daily ridership but also the appropriate settings for model estimation. In specific, the hypothesis testing demonstrates a method for determining the spatial weight matrix, estimation strategies for heterogeneous spatial and temporal units, and the minimum sample size required for reliable parameter estimates. Moreover, the study identifies that daily ridership is mainly influenced by number of employees, vehicle ownership, density of developed area, density of transit stations, density of parking space, bike-rack density, day of the week, and gasoline price. The empirical analyses are expected to be useful not only for researchers while developing and estimating models of taxi ridership but also for policy makers while understanding interactions between the traditional and emerging app-based taxi services.
This paper aims to explore the potential of carsharing in replacing private car trips and reducing car ownership and how this is affected by its attributes. To that affect, a stated choice experiment is conducted and the data are analyzed by latent class models in order to incorporate preference heterogeneity. The results show that around 40% of car drivers indicated that they are willing to replace some of their private car trips by carsharing, and 20% indicated that they may forego a planned purchase or shed a current car if carsharing becomes available near to them. The results further suggest that people vary significantly with respect to these two stated intentions, and that a higher intention of trip replacement does not necessarily correspond to higher intention of reducing car ownership. Our results also imply that changing the system attributes does not have a substantial impact on people’s intention, which suggests that the decision to use carsharing are mainly determined by other factors. Furthermore, deploying electric vehicles in carsharing fleet is preferred to fossil-fuel cars by some segments of the population, while it has no negative impact for other segments.
In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations.
In this paper, a spatial Markov chain model is developed to model the charging load of EVs in cities. The model assumes three distinct charging profiles: Work, Home, and Other. Geospatial maps were used to estimate the charging profile, or mixture of profiles, of the charging stations based on the nearby building types.
A case study was made on the city of Uppsala, Sweden—a city with approximately 44,000 cars. The results of the case study indicated that the aggregate load of the EVs in the city reduced the charging impact. For example when using 22 kW chargers, the peak load in the city per EV was estimated to be 1.29 kW/car in case of spatio-temporally opportunistic charging, and 1.47 kW/car in case of residential only opportunistic charging. This is to say that the Swedish grid operators can expect that every EV in the city will increase the peak load by at most 1.47 kW due to aggregation; this is assuming that 22 kW chargers were used.
In addition, we showed that the minute-minute variability of the charging load in cities might cause some future challenges. In our case, up to 3% of the EVs in the city simultaneously started charging. This caused a one-minute-ramp in the charging load of 1.1 MW—if charging using 3.7 kW. Charging with higher powers will exacerbate these ramps, e.g., charging with 22 kW will cause sudden one-minute-increases as high as 6.7 MW in the charging load. Such a finding indicates that using high charging powers might cause high variability in the charging load of EVs in cities. This high variability might limit the synergy potentials between EVs and variable renewable energy sources (RES).
The proposed model can potentially be used along with RES models to estimate the spatio-temporal synergy potentials between the two technologies. Evaluating the synergy potentials might be of value to grid operators, policy makers, market traders, etc.
This study is the first to systematically and quantitatively explore the factors that determine
the length of charging sessions at public charging stations for electric vehicles in urban areas, with
particular emphasis placed on the combined parking- and charging-related determinants of connection
times. We use a unique and large data set – containing information concerning 3.7 million charging
sessions of 84,000 (i.e., 70% of) Dutch EV-users – in which both private users and taxi and car sharing
vehicles are included; thus representing a large variation in charging duration behavior. Using
multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging
duration behavior across charging stations. We show how these explanatory variables can be used to
predict EV-charging behavior in urban areas and we derive preliminary implications for policy-makers
and planners who aim to optimize types and size of charging infrastructure.
Fuel cell vehicles and carsharing depict two potential solutions with regard to pollution and noise from traffic in cities. They are most effective when combined, and hydrogen is produced via electrolysis using renewables. One major hurdle in utilizing fuel cell vehicles is to size hydrogen refueling stations (HRS) and hydrogen production via electrolysis properly in order to fulfill the carsharing vehicles' demand at any given time. This paper presents data on refueling behavior in free-floating carsharing, which have not been available thus far. Refueling profiles of hydrogen carsharing vehicles are modeled based on this data. Furthermore, this analysis presents and applies a methodology for optimizing topology of a wind turbine-connected HRS with onsite electrolysis via an evolutionary algorithm. This optimization is conducted for different carsharing fleet sizes, and HRS profitability is evaluated. The results show that larger fleets are capable of decreasing hydrogen production costs significantly. Moreover, adding capacity to the HRS in order to prepare for hydrogen demand from private vehicles in the future does not significantly increase costs. However, overall costs are still high compared to the current market price in Germany, requiring further cost reductions.
Taxi is an indispensable mode in the urban public transportation. Although many studies have explored the travel patterns of taxi trips, few have combined taxi and subway to reveal their intermodal relationship. To bridge the gap, this study utilized taxi’s trajectory data to investigate its relationship with subway. Considering the multifaceted relationship between taxi and subway in operation, taxi trips are categorized into three types, namely, subway-competing, subway-extending, and subway-complementing taxi trips. The characteristics of each type of taxi trips reflect the specialties and their interactions with subway. The origin/destination distributions of taxi and subway trips are compared and analyzed. Furthermore, the supply and demand of taxi within the buffer zone of each subway station are analyzed to reflect the difficulty of hailing taxis. The negative binomial regression models are used to explore the relationship between taxi trips and subway ridership. The results show that there is a significantly positive correlation between taxi trips and subway ridership.
Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.
The charging load modelling of electric vehicles (EVs) is of great importance for safe and stable operation of power systems. However, it is difficult to use the traditional Monte Carlo method and mathematical optimization methods to establish a detailed and precise charging load model for EVs in both the temporal and spatial scales, especially for plug-in electric taxis (PETs) due to its strong random characteristics and complex operation behaviors. In order to solve this problem, multiple agents and the multi-step Q(λ) learning are utilized to model the charging loads of PETs in both the temporal and spatial scales. Firstly, a multi-agent framework is developed based on java agent development framework (JADE), and a variety of agents are built to simulate the operation related players, as well as the operational environment. Then, the multi-step Q(λ) learning is developed for PET Agents to make decisions under various situations and its performances are compared with the Q-learning. Simulation results illustrate that the proposed framework is able to dynamically simulate the PET daily operation and to obtain the charging loads of PETs in both the temporal and spatial scales. The multi-step Q(λ) learning outperforms Q-learning in terms of convergence rate and reward performance. Moreover, the PET shift strategies and electricity pricing mechanisms are investigated, and the results indicate that the appropriate operation rules of PETs significantly improve the safe and reliable operation of power systems.
Electric vehicle sharing (EVS) and bike sharing have been recognized as promising solutions to growingly serious problems of traffic congestion, air pollution, and insufficient parking spaces. This paper, based on the current research status, analyzes and evaluates the effect of shared transportation on alleviating or solving the traffic and environmental issues of mega-cities from five perspectives-resource, environment, convenience, economy and governance. Then, taking Beijing as a case, one of the representative giant cities, this paper investigates and analyzes the development situation, feasibility, and adaptation of shared transportation. Compared with 2015, the share of cars decreased by more than 3.2%, and the trip frequency decreased by more than 55% for private car owners in 2016. Shared transportation reduced energy consumption, nitrogen emissions and PM 2.5 by 45 million liters of gasoline, 540,000 tons and 4.5 billion mg just in one year in Beijing in 2016. The results indicate that shared transportation has an important role and potential in alleviating traffic and environmental issues of mega-cities. A series of development suggestions and some practical considerations are provided at the end, which might supply good decision-making guidance for policy makers.