Showing papers in "Transportation Research Part D-transport and Environment in 2021"

The role of micro-mobility in shaping sustainable cities: A systematic literature review

Abstract: Micro-mobility is increasingly recognised as a promising mode of urban transport, particularly for its potential to reduce private vehicle use for short-distance travel. Despite valuable research contributions that represent fundamental knowledge on this topic, today’s body of research appears quite fragmented in relation to the role of micro-mobility as a transformative solution for meeting sustainability outcomes in urban environments. This paper consolidates knowledge on the topic, analyses past and on-going research developments, and provides future research directions by using a rigorous and auditable systematic literature review methodology. To achieve these objectives, the paper analysed 328 journal publications from the Scopus database covering the period between 2000 and 2020. A bibliographic analysis was used to identify relevant publications and explore the changing landscape of micro-mobility research. The study constructed and visualised the literature’s bibliometric networks through citations and co-citations analyses for authors, articles, journals and countries. The findings showed a consistent spike in recent research outputs covering the sustainability aspects of micro-mobility reflecting its importance as a low-carbon and transformative mode of urban transport. The co-citation analysis, in particular, helped to categorise the literature into four main research themes that address benefits, technology, policy and behavioural mode-choice categories where the majority of research has been focused during the analysis period. For each cluster, inductive reasoning is used to discuss the emerging trends, barriers as well as pathways to overcome challenges to wide-scale deployment. This article provides a balanced and objective summary of research evidence on the topic and serves as a reference point for further research on micro-mobility for sustainable cities.

Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership

Abstract: The COVID-19 pandemic has led to a globally unprecedented decline in transit ridership. This paper leveraged the 20-years daily transit ridership data in Chicago to infer the impact of COVID-19 on ridership using the Bayesian structural time series model, controlling confounding effects of trend, seasonality, holiday, and weather. A partial least square regression was then employed to examine the relationships between the impact of ridership and various explanatory factors. Results suggested: (1) COVID-19 pandemic exerted significant effects on 95% of transit stations, leading to an average 72.4% drop in ridership. (2) Ridership declined more in regions with more commercial lands and higher percentages of white, educated, and high-income individuals. (3) Regions with more jobs in trade, transportation, and utility sectors presented smaller declines. (4) Regions with more COVID-19 cases/deaths presented smaller declines in transit ridership. Findings provide a timely understanding of the significantly reduced ridership during the pandemic and help transit agencies adjust services across different socioeconomic groups and space to better constrain virus transmission.

Energy consumption models for delivery drones: A comparison and assessment

Abstract: Energy consumption is a critical constraint for drone delivery operations to achieve their full potential of providing fast delivery, reducing cost, and cutting emissions. This paper provides a uniform framework to facilitate understanding different drone energy consumption models and the inter-relationships between key factors and performance measures to facilitate decision making for drone delivery operations. We review, classify and assess drone energy consumption models. We then document the very wide variations in the modeled energy consumption rates resulting from differences in: (1) the scopes and features of the models; (2) the specific designs of the drones; and (3) the details of their assumed operations and uses. The results show that great care must be taken in adopting a particular drone energy consumption model and that more research is needed, especially empirical research, to ensure the selected model accurately reflects delivery drone designs and uses.

The climate change mitigation effects of daily active travel in cities

Abstract: Active travel (walking or cycling for transport) is considered the most sustainable form of personal transport. Yet its net effects on mobility-related CO2 emissions are complex and under-researched. Here we collected travel activity data in seven European cities and derived life cycle CO2 emissions across modes and purposes. Daily mobility-related life cycle CO2 emissions were 3.2 kgCO2 per person, with car travel contributing 70% and cycling 1%. Cyclists had 84% lower life cycle CO2 emissions than non-cyclists. Life cycle CO2 emissions decreased by −14% per additional cycling trip and decreased by −62% for each avoided car trip. An average person who ‘shifted travel modes’ from car to bike decreased life cycle CO2 emissions by 3.2 kgCO2/day. Promoting active travel should be a cornerstone of strategies to meet net zero carbon targets, particularly in urban areas, while also improving public health and quality of urban life.

Eco-driving-based cooperative adaptive cruise control of connected vehicles platoon at signalized intersections

Abstract: Vehicle driving patterns greatly impact the sustainability of the transportation system. Based on V2X communication, the ecological cooperative adaptive cruise control (Eco-CACC) is proposed combing the advantages of eco-driving and car-following to minimize the energy consumption of the connected automated vehicles platoon. Herein, the vehicle platoon behavior in the scenario of driving through a signalized intersection exhibits great benefits for sustainability which is even improved along corridors with more traffic lights. In the velocity trajectory planning process, a modified dynamic programming algorithm is formulated with the switching logic gate of two types of optimal control problems to increase the computational speed. By testing in the real-world scenario, the results of the proposed Eco-CACC demonstrate excellent energy performance which improves 8.02% compared to manual driving with the constant acceleration policy. Moreover, energy can be further improved by 2.02% and 1.55% when the car-following strategy is selected with MPC and IDM algorithm.

Analysis of attitudes and engagement of shared e-scooter users

Abstract: Micromobility and especially e-scooter sharing have recently attracted a lot of attention, due to the rapid spreading of e-scooters in many cities around the world. However, many local authorities have not yet been prepared for efficiently integrating e-scooters in their transport systems and the exact impact of e-scooters is still unclear. It is therefore essential to understand the way e-scooters operate and their users’ profile. To address these questions, a study was designed based on 578 questionnaires (271 by e-scooter users and 307 by non-users) in the city of Thessaloniki, Greece. The analysis utilized a classification tree model for identifying the characteristics of people that are attracted by e-scooters (i.e., used them more than once) and a latent variable logit model for understanding the attributes of the regular e-scooter users. The results show that shared e-scooters mostly replaced walking and public transport trips; therefore, the positive impact of e-scooters on the environment is questioned. Also, the results indicate that people traveling with bicycle or motorcycle were not at all attracted by e-scooters. Moreover, females seem to be less keen on using e-scooters compared to males, while people living downtown are more regular users compared with those living in longer distances from the city center. These findings can aid policymakers in shaping the manner with which e-scooters can be incorporated in their cities.

Relating consumers' information and willingness to buy electric vehicles: Does personality matter?

Abstract: Being an energy-efficient mode of transportation, electric vehicles (EVs) adoption is a multifaceted mechanism driven by a bunch of factors. However, studies focusing on assessing the influence of personality traits on consumers' information about EVs (CINEVs) and willingness to buy (WTB) EVs are scarce. This study investigates the relationship between CINEVs and WTBEVs by accommodating the moderating role of the Big Five personality traits. Results are based on a sample of 624 respondents in the seven largest Indian cities by employing a comprehensive questionnaire survey. Structural equation modeling is used to test the formulated hypotheses. The results highlight that CINEVs is directly related to WTBEVs. We further add to the existing pool of knowledge by providing empirical evidence that openness, conscientiousness, extraversion, and agreeableness positively moderate the relationship between CINEVs and WTBEVs, whereas neuroticism negatively moderates this relationship. The results uncovered an interesting role of personality traits in propagating EV development.

Who is using e-scooters and how? Evidence from Paris

Abstract: Micromobility vehicles, and especially free-floating electric scooters (FFES), have been thriving over the past couple of years, Paris being the most important market worldwide In this paper, we first define micromobility Then, we present the design and results of an extensive face-to-face road survey among e-scooter (ES) users in Paris (N = 459, F(men) = 68%) Results indicate that ES users rarely own their proper microvehicle, are mostly men, aged 18–29, and have a high educational level They are not less motorized than the general population and use ES occasionally Their main motivation is travel time savings followed by playfulness and money savings However, users seeking money savings are not frequent riders They shifted mainly from walking and public transportation (72%) and few have increased their total mobility by making new trips (6%) Findings can be useful to researchers, policy makers, and FFES operators especially in the context of COVID pandemics

Electric scooter sharing: How do people value it as a last-mile transportation mode?

Abstract: With its advantages in urbanized areas, the electric scooter sharing (ESS) system has received considerable popularity as a transportation mode for short-distance trips. In this paper, people’s preferences toward ESS for last-mile trips were analyzed using a stated preference experiment. We designed the experiment to scrutinize how people value ESS-riding compared with other conventional last-mile transportation modes. The values of travel time components such as access and riding times are estimated using a mixed logit model. The values of riding transportation modes were significantly greater than that of walking for a last-mile trip. However, none of the time values of two modes—those from a conventional last-mile mode and ESS—was dominating because the difference in the dispersions of each made the time value curves intersect around their medians. Considering the results, we have concluded that ESS is a competitive transportation mode in last-mile situations.

Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Abstract: Ridesplitting, a form of ridesourcing services that matches riders with similar routes to the same driver, is a high occupancy travel mode that can bring considerable benefits. However, the current ratio of ridesplitting in the ridesourcing services is relatively low and its influencing factors remain unrevealed. Therefore, this paper uses a machine learning method, gradient boosting decision tree (GBDT) model, to explore the nonlinear effects of built environment on the ridesplitting ratio of origin–destination pairs (census tract to census tract). The GBDT model also provides the relative importance ranking of all the built environment factors. The results indicate that distance to city center, land use diversity and road density are the key influencing factors of ridesplitting ratio. In addition, the non-linear thresholds of built environment factors are identified based on partial dependence plots, which could provide policy implications for the government and transportation network companies to promote ridesplitting.

Consumer acceptance of shared e-scooters for urban and short-distance mobility

Abstract: E-scooters have conquered urban areas as a means for individual mobility and compete with other modes of transportation. While some studies endorse e-scooters as eco-friendly solution for crowded cities, others report contradictory findings and highlight safety issues. To reveal factors affecting e-scooter usage from a consumer’s perspective, a study using an adapted Unified Theory of Acceptance and Use of Technology (UTAUT2) is conducted. Based on random sampling among German public transportation services, 749 responses were collected and analyzed. E-scooters are studied in the context of mobility alternatives, revealing that they are mostly viewed as fun objects, and perceived safety indeed impedes their usage. Additionally, environmental concerns and individual convenience (i.e., performance expectancy) evince to represent the main drivers for using e-scooter. Besides, differences in the motivation for (potential) usage were found between owners and non-owners. Regarding the ecological assessment of e-scooters, they may, in fact, substitute walking over short distances.

Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior

Abstract: Electric vehicles are a sustainable substitution to conventional vehicles. This study introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

Transportation technologies, sharing economy, and teleactivities: Implications for built environment and travel

Abstract: This paper reviews how teleactivities, the sharing economy, and emerging transportation technologies – components of what we could call the “App City” – may influence travel behavior and the built environment. Findings suggest that teleactivities may substitute some trips but generate others. Telework and teleconferencing may reduce total travel. Findings on the sharing economy suggest that accommodation sharing increases long-distance travel; bikesharing is conducive to more active travel and lower car use; carsharing may reduce private car use and ownership; ridesourcing (ridehailing) may increase vehicle miles traveled; while the implications of e-scooter sharing, ridesharing, and Mobility as a Service are context-dependent. Findings on emerging transportation technologies suggest that private autonomous vehicles and urban air mobility may increase total travel, whereas autonomous buses may lead to reduced car use. Implications of App Cities for the built environment include new transport systems and land use changes due to behavioral changes.

Optimal charging strategy for large-scale electric buses considering resource constraints

Abstract: The large-scale application of electric buses highlights a series of practical problems, such as high charging costs, unreasonable utilization of charging resources, and chaotic charging schedules. In this study, a mixed integer programming model was first proposed to reduce the total charging cost of electric bus fleets by optimizing the charging power and charging time. Meanwhile, to improve the efficiency of the model under large-scale charging scenarios, a column-generation-based algorithm is developed to decompose the original model into a master problem and several subproblems, where each electric bus’s charging strategy is solved in an independent subproblem to guarantee a stable operational bus fleet schedule and to avoid intermittent charging. An experimental analysis is carried out. The results show that the optimal charging strategy can reduce the charging cost by approximately 36.1% compared with the uncontrolled charging strategy, which carries the potential to be applied in large-scale bus fleet operation.

Battery Electric Vehicle adoption in regions without strong policies

Abstract: Individual motorized transport is a major source of emissions and needs to be reduced to meet international agreements. Although alternatives to internal combustion engine vehicles are already on the market, without extensive political support, electric vehicle (EV) adoption remains low. Understanding the drivers of adoption of alternative technologies is key to develop effective measures to accelerate their diffusion. This paper presents individual consumer characteristics and home-location based spatial characteristics of current battery electric vehicle (BEV) and internal combustion engine vehicle holders, in a region free from strong EV policies. Using a generalized linear mixed-effects logistic model on this revealed preference data, we find that BEV adoption is predicted by technology affinity, high income, green party preferences, and living in one’s own house. Altogether, the study offers insights on the characteristics of early adopters of BEVs that can be valuable to policymakers, energy grid and charging infrastructure operators, as well as the automotive industry.

Transport infrastructure and CO2 emissions in the OECD over the long run

Abstract: A gap in the transportation-environment literature is the absence of studies analysing the effect of transport infrastructure on carbon dioxide (CO2) emissions, controlling for other factors correlated with CO2 emissions. We address this gap by providing parametric and non-parametric estimates of the effect of transport infrastructure on CO2 emissions for a panel of OECD countries over a period of almost 150 years. We also examine economic growth and population as channels through which transport infrastructure influences CO2 emissions. Our point estimates suggest that a 1% increase in transport infrastructure is associated with an increase in CO2 emissions of about 0.4%, although dependent on the long-run estimator used. Our non-parametric estimates suggest a time-varying relationship between transport infrastructure and CO2 emissions, which was positive during the first wave of globalisation, World War II and for most of the period since 1950. We find that economic growth and population mediate the transport infrastructure CO2 emissions relationship.

Carbon footprint impacts of banning cars with internal combustion engines

Abstract: Banning sales of passenger cars with internal combustion engines is becoming a common climate change mitigation policy. This study analyzes the effects of such a ban on the carbon footprints of passenger car travel in Sweden using a novel vehicle turnover model and prospective lifecycle assessment, with scenarios for decarbonization of supply chains. A ban on internal combustion engines results in significantly decreased carbon footprints primarily due to reduced tailpipe CO2 emissions. The full effect of a ban is delayed due to fleet inertia. Increasing the pace of electrification is beneficial for the carbon footprint regardless of global manufacturing decarbonization pathways. A ban in 2030 is not sufficient to reach national policy targets for the transport sector, requiring either an earlier ban (i.e., 2025) or increased biofuel use. Risks of carbon leakage may motivate extending current regulations of vehicle-specific tailpipe emissions to also cover carbon footprints for new cars.

Who uses shared micro-mobility services?: Empirical evidence from Zurich, Switzerland

Abstract: Shared micro-mobility services have rapidly gained popularity yet challenged city administrations to develop adequate policies while scientific insight is largely missing. From a transportation equity perspective, it is particularly important to understand user correlates, as they are the beneficiaries from public investment and reallocation of public space. This paper provides an up-to-date account of shared micro-mobility adoption and user characteristics in Zurich, Switzerland. Our results suggest that shared micro-mobility users tend to be young, university-educated males with full-time employment living in affluent households without children or cars. Shared e-scooter users, in particular, are younger, yet more representative of the general population in terms of education, full-time employment, income and gender than bike-sharing users. This suggests that shared e-scooters may contribute to transportation equity, yet their promotion should be handled with care as life-cycle emissions exceed those of bike-sharing and equity contributions might be skewed as many users are students.

Electric vehicle charging network in Europe: An accessibility and deployment trends analysis

Abstract: If coupled with a low-carbon electricity mix, electric vehicles (EVs) can represent an important technology for transport decarbonization and local pollutants abatement. Yet, to ensure large-scale EVs adoption, an adequate charging stations network must be developed. This paper provides the first comprehensive bottom-up analysis of the EV charging network in Europe. Combining a crowd-sourced database of charging stations with accessibility data and algorithms, we produce maps of the travel time to the most accessible EV charging station across Europe, we evaluate the charging points density and the number of active operators in different areas. We find that although recent years have witnessed a notable expansion of the EV charging network, stark inequalities persist across and within countries, both in terms of accessibility and of the charging points available to users. Our results allow for a better understanding of some of the key challenges ahead for ensuring mass EVs adoption throughout Europe and thus potentially reducing the environmental impact of the transport sector.

Location and capacity decisions for electric bus charging stations considering waiting times

Abstract: This research proposes a mixed integer-linear mathematical model for location and capacity decisions of electric bus charging stations in order to ensure the connectivity of the road network throughout a certain region. The routes followed by electric buses in a country, demand in each route and driving ranges of electric buses are considered so as to determine the locations and capacities of charging stations under limited waiting time constraints. We implement the model on a case study for intercity bus networks in Turkey and use the actual data of coach companies. The results provide optimal locations and capacities of charging stations with minimum cost. Moreover, sensitivity analysis is performed to analyze the effects of different parameters on the results. It is observed that driving ranges have the highest importance in the efficient use of electric buses, and charging durations, number of trips and service rates significantly affect capacities of stations.

What type of infrastructures do e-scooter riders prefer? A route choice model

Abstract: E-scooter is an innovative travel mode that meets the demand of many travelers. A lack of understanding of user routing preferences makes it difficult for policymakers to adapt existing infrastructures to accommodate these emerging travel demands. This study develops an e-scooter route choice model to reveal riders’ preferences for different types of transportation infrastructures, using revealed preferences data. The data were collected using Global Positioning System units installed on e-scooters operating on Virginia Tech’s campus. We applied the Recursive Logit route choice model to 2000 randomly sampled e-scooter trajectories. The model results suggest e-scooter riders are willing to travel longer distances to ride in bikeways (59% longer), multi-use paths (29%), tertiary roads (15%), and one-way roads (21%). E-scooter users also prefer shorter and simpler routes. Finally, slope is not a determinant for e-scooter route choice, likely because e-scooters are powered by electricity.

Home-deliveries Before-During COVID-19 Lockdown: Accessibility, Environmental Justice, Equity, and Policy Implications.

Abstract: During the COVID-19 lockdowns, home deliveries have changed from being a desirable luxury or comfortable solution to a health-supporting and essential service for many COVID-19 at-risk populations. However, not all households are equal in terms of access to home deliveries. The onset of COVID-19 has brought to light access inequalities that preceded the pandemic and that the COVID-19 lockdown has exacerbated and made visible. The concept of home-based accessibility (HBA) is introduced, and novel research questions are addressed: (i) What type of households had zero home deliveries before COVID-19 lockdown? (ii) How the COVID-19 lockdown affected the type of households that receive home deliveries? and (iii) What are the implications of no access to home delivery services in terms of equity and environmental justice? To answer the first two questions, exploratory and confirmatory models with latent variables are estimated utilizing data collected from an online survey representative of the population in the Portland metropolitan region. Policy and environmental equity implications are discussed using the concept of home-based accessibility (HBA). The results indicate that traditionally underserved populations are less likely to benefit from home-based delivery services and that COVID-19 has worsened home delivery inequalities for underserved populations.

Reduction of maritime GHG emissions and the potential role of E-fuels

Abstract: Maritime transport accounts for around 3% of global anthropogenic Greenhouse gas (GHG) emissions (Well-to-Wake) and these emissions must be reduced with at least 50% in absolute values by 2050, to contribute to the ambitions of the Paris agreement (2015). Zero carbon fuels made from renewable sources (hydro, wind or solar) are by many seen as the most promising option to deliver the desired GHG reductions. For the maritime sector, these fuels come in two forms: First as E-Hydrogen or E-Ammonia; Second as Hydrocarbon E-fuels in the form of E-Diesel, E-LNG, or E-Methanol. We evaluate emissions, energy use and cost for E-fuels and find that the most robust path to these fuels is through dual-fuel engines and systems to ensure flexibility in fuel selection, to prepare for growing supplies and lower risks. The GHG reduction potential of E-fuels depends entirely on abundant renewable electricity.

Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity

Abstract: Understanding intermodal transit trip generation is essential to increase the share of long-distance transit trips among urban transportation systems. Although many studies have investigated trip generation, the existing literature still has limited evidence about intermodal transit trips and their nonlinear associations with the built environment over space. This study proposes a decision framework to identify the mean relative importance of socioeconomic attributes and built environment elements as well as their effective ranges and threshold effects at the spatial scale. An empirical study was conducted using large-scale smart card data in Nanjing, China. The modeling results indicate the proposed hybrid model can significantly enhance the predictive power, as compared to traditional models. The mean relative importance of the distance to the nearest metro station ranks the highest among all attributes studied, followed by bus route and land use mix. The effective ranges and thresholds of most built environment elements vary spatially with the upper quartile zones being the largest.

Workability of rubberized asphalt from a perspective of particle effect

Abstract: Previous studies have documented the disconnect between the rotational viscosity of asphalt rubber (AR) and the workability of AR mix. There is a possibility that the particle effect of crumb rubber modifier (CRM) particles within AR causes this phenomenon. This study measured the viscosities of ARs and the corresponding liquid phases at three temperatures. The workability of the corresponding AR mixes was evaluated using the number of gyrations of Superpave Gyratory Compactor (SGC) samples. The changes on AR caused by the CRM contents were measured using Gel permeation chromatography (GPC) test. The experimental results revealed that the rotational viscosity of the AR liquid phase is more suitable to be used as a workability indicator. In addition, thresholds of CRM content were reported. Once the threshold is exceeded, the increase in AR viscosity is only caused by the increase in CRM particle effect and there is no detectable CRM/asphalt interaction.

Variation in ride-hailing trips in Chengdu, China

Abstract: We investigate various spatial, economic, and land-use factors associated with the generation of trips for ride-hailing services in Chengdu, China. Using one month of data for DiDi Chuxing trips in Chengdu, we characterize the unique pattern of TNC ride-hailing trips over space and for different time periods. We examine the association between the generation of ride-hailing trips and spatial characteristics, including population density, floor-area ratio, housing prices, road networks, the proximity of public transit, land use mix, and points of interests. We estimate “global” regression models and “local” Geographically Weighted Regression models that account for the spatial variation of each factor on trip generation. Results suggest that population density, local road density, floor-area ratio, housing price, and the proximity to subways have positive associations with DiDi trip generation. We also examine the spatial variation associated with the local population density coefficients which vary throughout the city.

Who goes electric? The anatomy of electric car ownership in Norway

Abstract: We describe the anatomy of electric car ownership in Norway, the country with the highest market share of low-emission vehicles, using matched administrative micro data covering the entire population of private car owners Our results show that socioeconomic characteristics are strong predictors of the car portfolio Battery electric vehicle (BEV) ownership is increasing in wealth, income and education While early BEV owners differed from other car owners, over time BEV owners have become more similar to other car owners We document a strong association between BEV privileges on the travel to work (like toll road exemptions and bus lane access) and BEV ownership We show that BEV buyers are less likely than other car buyers to sell their old car, but this difference has diminished over time

Energy and environmental efficiency of China’s transportation sectors considering CO2 emission uncertainty

Abstract: China’s transportation sector suffers from energy over-consumption and CO2 over-emission, resulting in increasing pressure to improve energy and environmental efficiency. Current measurement techniques cannot produce precise CO2 emission data, and this uncertainty makes previous approaches problematic for analyzing energy and environmental efficiency. This study combines stochastic multicriteria acceptability analysis (SMAA-2) with data envelopment analysis (DEA) to evaluate the energy and environmental efficiency of Chinese transportation sectors in the presence of uncertain CO2 emission data. The improved SMAA-DEA approach effectively handles CO2 data uncertainty and also considers all possible input and output weights, thus providing meaningful information (such as maximum efficiency, average efficiency, and rank acceptability index) to guide the development of effective policies to improve efficiency. This study’s empirical findings show that the energy and environmental efficiency of transportation sectors in 30 provincial regions is poor, great efficiency disparities exist between regions, and uneven development has occurred in China.

How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China

Abstract: Ridespitting, which enables riders with similar routes to share a ridesourcing trip, is a promising transportation technology to reduce traffic congestions and air pollutions. This study aims to explore how ridesplitting reduces emissions from ridesourcing based on GPS trajectory data from Didi Chuxing in Chengdu, China. First, this study quantifies the emission factors of both regular ridesourcing and ridesplitting trips to evaluate the emission reductions per ride-km from ridesplitting. The results show that the average emission reduction rates of CO2, CO, NOx, and HC are 28.7%, 32.5%, 27.7%, and 31.2%, respectively. Then, a spatiotemporal analysis of the emission reductions indicates that ridesplitting generally reduces more emissions around the expressways and during peak hours. Finally, a spatial error model is adopted to analyze the effects of travel-related and built environment variables on emission reductions from ridesplitting. The trajectory overlapping rate of shared rides turns out to be the most important determinant for expanding the environmental benefits of ridesplitting.

Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Abstract: Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet’s daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.