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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 39163

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Special Issue Editors

Dr. Junfeng Jiao

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Guest Editor

Urban Informatic Lab, School of Architecture, University of Texas-Austin, Austin, TX 78712, USA
Interests: smart cities; shared mobility; GIS and spatial analysis; urban and transportation planning

Dr. Amin Azimian

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Guest Editor

Dr. Haizhong Wang

E-Mail Website
Guest Editor

School of Civil and Construction Engineering, College of Engineering, Oregon State University, Corvallis OR 97331, USA
Interests: transportation system planning; travel behavior analysis

Special Issue Information

Dear Colleagues,

Shared mobility has grown significantly over the past few years as a result of increased commitment to creating more sustainable transportation as well as advances in location-based services, and mobile technologies. To date, several environmental, social, and transportation-related benefits have been reported from the use of shared mobility modes. For instance, numerous studies have reported economic benefits, and changes in vehicle usage, ownership, and vehicle miles traveled. However, most impacts are quantified in the context of public transit and carsharing.

With the fast development of technologies (e.g., big data, smart sensors) and emergence of micromobility devices (e.g., e-scooters, e-bikes), people are provided with more mobility choices to make short trips. Nevertheless, our understanding of their socio-economic impacts on our community remains limited. In addition, the extent to which the micromobility devices affect current and future urban infrastructures are unknown. Hence, an accurate assessment of socio-economic impacts will help governments and decision makers to manage and respond to the challenges of the transition from current to future states. This Special Issue aims to bring together theoretical and empirical studies on outcomes of new shared mobility and micromobility devices across urban areas around the world.

We invite papers that address a range of topics related to shared mobility and sustainable transportation. We are striving to contribute to the scholarly and practical knowledge relevant to both urban planning and sustainable transportation. Possible topics may include but are not limited to:

Socio-economic impacts of micromobility devices
Micromobility and potential changes in current and future urban infrastructures
Shared mobility and users’ characteristics
Shared mobility and air pollution;
Shared mobility and traffic safety;

To facilitate the development of this Special Issue, special sessions will be hosted at the International Association of China Planning (IACP) annual conference in July 2021 (tentative and subject to change). Please note, however, that this Special Issue is open to all scholars, regardless of their participation in the special sessions.

Dr. Junfeng Jiao
Dr. Amin Azimian
Dr. Haizhong Wang
Guest Editors

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Keywords

shared mobility
sustainable transportation
environment
travel behavior
economic impact

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Published Papers (11 papers)

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Research

19 pages, 1476 KiB

Open AccessArticle

Choosing a Mode in Bangkok: Room for Shared Mobility?

by Eva Ayaragarnchanakul, Felix Creutzig, Aneeque Javaid and Nattapong Puttanapong

Sustainability 2022, 14(15), 9127; https://doi.org/10.3390/su14159127 - 25 Jul 2022

Cited by 4 | Viewed by 2899

Abstract

Individual motorized vehicles in urban environments are inefficiently oversupplied both from the perspective of transport system efficiency and from the perspective of local and global environmental externalities. Shared mobility offers the promise of more efficient use of four-wheeler vehicles, while maintaining flexible routing. Here, we aim to understand the travel mode choices of commuters in Bangkok and explore the potential demand for shared mobility through examining both revealed and stated choices, based on our survey (n = 1239) and a systematic comparison of mode choice situations. Our multinomial logistic regression analysis indicates that commuters value time in their vehicles and accept fuel costs, but that they dislike wasting time walking, waiting, and searching for parking or pay for road use and parking. Our model results imply that shared taxi has a higher chance of being used as a door-to-door mode rather than as a competitor to motorcycle taxis as a feeder to the metro stations. Ride sharing gains substantial potential when private motorized cars are charged with the social external costs they cause via congestion charges and parking fees. Replacing cars with shared taxis as the daily choice for those living in detached houses will result in a 24–36% reduction of car trips on Bangkok roads. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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24 pages, 9246 KiB

Open AccessArticle

Bi Objective Peer-to-Peer Ridesharing Model for Balancing Passengers Time and Costs

by Seyed Omid Hasanpour Jesri and Mohsen Akbarpour Shirazi

Sustainability 2022, 14(12), 7443; https://doi.org/10.3390/su14127443 - 17 Jun 2022

Cited by 1 | Viewed by 1940

Abstract

Ride-sharing services are one of the top growing sustainable transportation trends led by mobility-as-a-service companies. Ridesharing is a system that provides the ability to share vehicles on similar routes for passengers with similar or nearby destinations on short notice, leading to decreased costs for travelers. At the same time, though, it takes longer to get from place to place, increasing travel time. Therefore, a fundamental challenge for mobility service providers should be finding a balance between cost and travel time. This paper develops an integer bi-objective optimization model that integrates vehicle assignment, vehicle routing, and passenger assignment to find a non-dominated solution based on cost and time. The model allows a vehicle to be used multiple times by different passengers. The first objective seeks to minimize the total cost, including the fixed cost, defined as the supply cost per vehicle, and the operating cost, which is a function of the distance traveled. The second objective is to minimize the time it takes passengers to reach their destination. This is measured by how long it takes each vehicle to reach the passenger’s point of origin and how long it takes to get to the destination. The proposed model is solved using the AUGMECON method and the NSGA II algorithm. A real case study from Sioux Falls is presented to validate the applicability of the proposed model. This study shows that ridesharing helps passengers save money using mobility services without significant change in travel time. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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18 pages, 819 KiB

Open AccessArticle

Ridesharing as a Potential Sustainable Transportation Alternative in Suburban Universities: The Case of Najran University, Saudi Arabia

by Saad AlQuhtani

Sustainability 2022, 14(8), 4392; https://doi.org/10.3390/su14084392 - 7 Apr 2022

Cited by 5 | Viewed by 4365

Abstract

In Saudi Arabia, car ownership rates are considered comparatively high due to the lack of other alternatives, cheap fuel and car registration costs, and higher income. The population relies mainly on automobiles for their daily trips and primarily commutes alone, contributing to many negative consequences. Therefore, ridesharing is a transportation mode that is a suitable approach in such an area, since it can increase the occupancy rates and reduce single-occupant driving, which in turn can cut vehicle emissions, contribute to a reduction in vehicle ownership and vehicle miles traveled, alleviate traffic congestions and accidents, and decrease the need for parking spaces. Suburban universities are considered major trip generators and attractors. They also can offer a niche market for ridesharing programs. Thus, data was obtained from a survey performed at Najran University to investigate the ridesharing behavior among the university population. Following a descriptive analysis of the commuter survey data, a binary logistic regression model was adopted to investigate the interest in ridesharing. The estimation results show being female and non-Saudi, as well as being students and faculty members in general (versus staff), along with the presence of fixed (regular) work or class schedules, increase the likelihood of ridesharing. Since the probability of most of the university population (i.e., students and faculty members) toward ridesharing is high, the number of automobiles needed by commuters will be reduced, resulting in a higher transition to environmentally sustainable urban mobility. In addition, the university has many motivators that can positively affect the propensity to rideshare, such as the lack of public transportation, fixed schedules, a longer distance to campus, and a similar social background among attendees; therefore, universities or other large employers can take these motivators into account when planning ridesharing services. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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25 pages, 8465 KiB

Open AccessArticle

Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

by Muhammad Safdar, Arshad Jamal, Hassan M. Al-Ahmadi, Muhammad Tauhidur Rahman and Meshal Almoshaogeh

Sustainability 2022, 14(5), 2778; https://doi.org/10.3390/su14052778 - 26 Feb 2022

Cited by 43 | Viewed by 6582

Abstract

Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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15 pages, 40625 KiB

Open AccessArticle

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

by Sujae Kim, Sangho Choo, Gyeongjae Lee and Sanghun Kim

Sustainability 2022, 14(5), 2564; https://doi.org/10.3390/su14052564 - 23 Feb 2022

Cited by 23 | Viewed by 3967

Abstract

The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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24 pages, 5554 KiB

Open AccessArticle

What Kind of Travellers Are Using Carsharing in Beijing? A Study Based on Selective Ensemble Learning

by Wei Luo, Yi Wang, Pengpeng Jiao, Zehao Wang and Pengfei Zhao

Sustainability 2022, 14(1), 540; https://doi.org/10.3390/su14010540 - 4 Jan 2022

Cited by 6 | Viewed by 2085

Abstract

As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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22 pages, 2181 KiB

Open AccessArticle

Spatial-Temporal Flows-Adaptive Street Layout Control Using Reinforcement Learning

by Qiming Ye, Yuxiang Feng, Eduardo Candela, Jose Escribano Macias, Marc Stettler and Panagiotis Angeloudis

Sustainability 2022, 14(1), 107; https://doi.org/10.3390/su14010107 - 23 Dec 2021

Cited by 5 | Viewed by 2667

Abstract

Complete streets scheme makes seminal contributions to securing the basic public right-of-way (ROW), improving road safety, and maintaining high traffic efficiency for all modes of commute. However, such a popular street design paradigm also faces endogenous pressures like the appeal to a more balanced ROW for non-vehicular users. In addition, the deployment of Autonomous Vehicle (AV) mobility is likely to challenge the conventional use of the street space as well as this scheme. Previous studies have invented automated control techniques for specific road management issues, such as traffic light control and lane management. Whereas models and algorithms that dynamically calibrate the ROW of road space corresponding to travel demands and place-making requirements still represent a research gap. This study proposes a novel optimal control method that decides the ROW of road space assigned to driveways and sidewalks in real-time. To solve this optimal control task, a reinforcement learning method is introduced that employs a microscopic traffic simulator, namely SUMO, as its environment. The model was trained for 150 episodes using a four-legged intersection and joint AVs-pedestrian travel demands of a day. Results evidenced the effectiveness of the model in both symmetric and asymmetric road settings. After being trained by 150 episodes, our proposed model significantly increased its comprehensive reward of both pedestrians and vehicular traffic efficiency and sidewalk ratio by 10.39%. Decisions on the balanced ROW are optimised as 90.16% of the edges decrease the driveways supply and raise sidewalk shares by approximately 9%. Moreover, during 18.22% of the tested time slots, a lane-width equivalent space is shifted from driveways to sidewalks, minimising the travel costs for both an AV fleet and pedestrians. Our study primarily contributes to the modelling architecture and algorithms concerning centralised and real-time ROW management. Prospective applications out of this method are likely to facilitate AV mobility-oriented road management and pedestrian-friendly street space design in the near future. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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19 pages, 1992 KiB

Open AccessArticle

The Spatial Effect of Shared Mobility on Urban Traffic Congestion: Evidence from Chinese Cities

by Jiachen Li, Mengqing Ma, Xin Xia and Wenhui Ren

Sustainability 2021, 13(24), 14065; https://doi.org/10.3390/su132414065 - 20 Dec 2021

Cited by 7 | Viewed by 4646

Abstract

This paper explores the spatial spillover effect of shared mobility on urban traffic congestion by constructing spatial econometric models. Based on panel data of 94 Chinese cities from 2016 to 2019, this study analyses the spatial correlation of shared mobility enterprise layout and geographical correlation of urban transport infrastructure and examines their influence mechanism. From the perspective of geographic spatial distribution, congestion has positive spatial correlation among Chinese cities, and it has different directions and centripetal forces across regions. The shared mobility enterprises in a region have same direction distribution with traffic congestion, but the centripetal forces of the aggregation effect are different. The econometric results include the fact that bike-sharing has reduced congestion significantly, but the overall impact of car-sharing is not clear. Neither bike-sharing nor car-sharing can offset the traffic congestion caused by economic activities and income growth. From the perspective of spillover effects, congestion has been influenced by bike-sharing, economic development, population, and public passengers in surrounding areas. In terms of spatial heterogeneity, bike-sharing relieves congestion in the Pearl River Delta region while having no significant effect in other regions. Meanwhile, car-sharing has aggravated congestion in the Yangtze River Delta but eased traffic jams in the Pearl River Delta. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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17 pages, 608 KiB

Open AccessArticle

Does Job Satisfaction Influence the Productivity of Ride-Sourcing Drivers? A Hierarchical Structural Equation Modelling Approach for the Case of Bandung City Ride-Sourcing Drivers

by Tri Basuki Joewono, Muhamad Rizki and Jeanly Syahputri

Sustainability 2021, 13(19), 10834; https://doi.org/10.3390/su131910834 - 29 Sep 2021

Cited by 10 | Viewed by 3127

Abstract

With various benefits being offered by ride-sourcing companies, Indonesian cities have experienced a substantial increase in the number of ride-sourcing drivers in the past five years. However, with tense working conditions, there is a question as to how drivers perceive their work satisfaction and how this satisfaction influences their productivity. This study aims to investigate the factors that influence ride-sourcing drivers’ job satisfaction and productivity. For this purpose, a questionnaire was distributed to ride-sourcing drivers in 2019 and analysed using hierarchical structural equation modelling (SEM). Wage competitiveness and financial safety are found to be appreciated the most by ride-sourcing drivers, while undertaking multiple jobs tends to be associated with low satisfaction. Satisfaction is also found to positively influence trip productivity. Drivers who perceive themselves as being exposed to health and safety risks tend to have lower satisfaction. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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13 pages, 2544 KiB

Open AccessArticle

Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas

by Junfeng Jiao, Shunhua Bai and Seung Jun Choi

Sustainability 2021, 13(19), 10583; https://doi.org/10.3390/su131910583 - 24 Sep 2021

Cited by 9 | Viewed by 2746

Abstract

Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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14 pages, 1250 KiB

Open AccessArticle

Re-Recognition of Ride-Sourcing Service: From the Perspective of Operational Efficiency and Social Welfare

by Zipeng Zhang and Ning Zhang

Sustainability 2021, 13(15), 8198; https://doi.org/10.3390/su13158198 - 22 Jul 2021

Cited by 1 | Viewed by 1803

Abstract

With increasing availability of alternative mobility options for city transportation system, it is necessary to better understand how emerging mobility options are impacting the travel demand and consumer-social surplus. However, few study have been conducted to evaluate the social welfare effects of the range of vacant trips in ride-sourcing service modes. This paper identified the vacant trip and loading rate evaluation model under the ride-sourcing service mode to enhance the effective operation of the different mobility services under numerical illustrations. The solution can also offer some beneficial guidance and theoretical basis for ride-sourcing systems in regard to planning and management aspects. Full article

(This article belongs to the Special Issue Shared Mobility and Sustainable Transportation)

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