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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 (31 July 2019) | Viewed by 55459

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Prof. Dr. Daniel Albalate del Sol

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Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Av. Diagonal 690, Torre 6, 08034 Barcelona, Spain
Interests: transport policy; infrastructure; policy evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cities worldwide face increasingly unsustainable mobility patterns as they become more dependent on the car. Urban transport becomes an essential challenge for local policy makers needing innovative policies sustained and supported by scientific research evidence that might help in the fight against traffic congestion, environmental degradation, climate change and health and safety perils. This Special Issue includes academic works providing evidence on new paradigms of sustainable transportation policy for cities. Sustainable transportation covers environmental, social and economic dimensions and require a multi-disciplinary approach to examine, explore and critically engage with issues and advances leading to more sustainable cities. A variety of topics and policy measures are welcome: reduction of fossil fuels’ dependence, promotion of renewal and regenerated energy technology—as electric vehicles and their challenges and opportunities—regulation and planning, provision of new transportation modes, collaborative and shared economy initiatives, etc. The issue aims to encourage an informed and rigorous debate on sustainable mobility and transport policies in cities.

Prof. Dr. Daniel Albalate
Guest Editor

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Keywords

Sustainability
Transportation
Mobility
Cities
Transport Policy

Published Papers (13 papers)

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Research

21 pages, 830 KiB

Open AccessArticle

Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas

by Daniel Albalate and Xavier Fageda

Sustainability 2019, 11(18), 5092; https://doi.org/10.3390/su11185092 - 18 Sep 2019

Cited by 41 | Viewed by 8713

Abstract

Congestion and road accidents are both considered essential challenges for sustainable mobility in large cities, but their relationship is only partially explored by the literature. In this paper, we empirically examine different public policies aimed at reducing urban traffic congestion but which may also have indirect effects on road accidents and casualties. We use data from 25 large urban areas in Spain for the period 2008–2017 and apply econometric methods to investigate how a variety of public policies do affect both negative externalities. Although the relationship between congestion and road safety is complex, we find that the promotion of certain modes of public transportation and the regulation of parking spaces may contribute to making cities more sustainable, both in terms of the time spent traveling and the probability of being affected by an accident. Considering whether policies addressing congestion improve or damage road safety as an indirect result is a useful approach for local policy-makers and planners in their attempt to get sustainable transportation outcomes. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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16 pages, 2376 KiB

Open AccessArticle

Logarithmic Mean Divisia Index Decomposition of CO₂ Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001

by Meiting Tu, Ye Li, Lei Bao, Yuao Wei, Olivier Orfila, Wenxiang Li and Dominique Gruyer

Sustainability 2019, 11(16), 4310; https://doi.org/10.3390/su11164310 - 09 Aug 2019

Cited by 15 | Viewed by 4154

Abstract

The urban transport sector has become one of the major contributors to global CO₂ emissions. This paper investigates the driving forces of changes in CO₂ emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO₂ emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO₂ emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO₂ emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita; (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO₂ emissions, and these changes should not be abandoned when oil prices fall; and (iii) cities with high population densities should focus on the development of public and non-motorized transport. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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23 pages, 5281 KiB

Open AccessArticle

Influence Area of Transit-Oriented Development for Individual Delhi Metro Stations Considering Multimodal Accessibility

by Sangeetha Ann, Meilan Jiang and Toshiyuki Yamamoto

Sustainability 2019, 11(16), 4295; https://doi.org/10.3390/su11164295 - 08 Aug 2019

Cited by 7 | Viewed by 3620

Abstract

Understanding the influence areas for transit stations in Indian cities is a prerequisite for adopting transit-oriented development (TOD). This study provides insights into the last mile patterns for selected Delhi Metro Rail (DMR) stations, specifically, Karkardooma, Dwarka Mor, Lajpat Nagar, and Vaishali, and the extent of the influence area based on different access modes. The variation in the extent of the influence areas based on various modes and the locational characteristics of stations have been considered in this study. The last mile distances reported in the conducted survey involved the problems of rounding and heaping, and they were subjected to multiple imputation to remove the bias. The spatial extent of the influence areas for various modes was estimated based on the compound power exponential distance decay function. Further, the threshold walking distances were calculated using the receiver operating characteristic (ROC) curves. The variations were noted in the last mile distances among stations. The walking distances (mean and 85th percentile) among stations did not vary considerably; however, large variations were noted when comparing other modes. These differences in accessibility must be taken into account when considering multimodal accessibility and multimode-based TOD. The study can provide useful inputs for planning and implementing TOD in New Delhi. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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11 pages, 1737 KiB

Open AccessArticle

Congestion Control in Charging Stations Allocation with Q-Learning

by Li Zhang, Ke Gong and Maozeng Xu

Sustainability 2019, 11(14), 3900; https://doi.org/10.3390/su11143900 - 17 Jul 2019

Cited by 17 | Viewed by 2298

Abstract

Navigation systems can help in allocating public charging stations to electric vehicles (EVs) with the aim of minimizing EVs’ charging time by integrating sufficient data. However, the existing systems only consider their travel time and transform the allocation as a routing problem. In this paper, we involve the queuing time in stations as one part of EVs’ charging time, and another part is the travel time on roads. Roads and stations are easily congested resources, and we constructed a joint-resource congestion game to describe the interaction between vehicles and resources. With a finite number of vehicles and resources, there exists a Nash equilibrium. To realize a self-adaptive allocation work, we applied the Q-learning algorithm on systems, defining sets of states and actions in our constructed environment. After being allocated one by one, vehicles concurrently requesting to be charged will be processed properly. We collected urban road network data from Chongqing city and conducted experiments. The results illustrate the proposed method can be used to solve the problem, and its convergence performance was better than the genetic algorithm. The road capacity and the number of EVs affected the initial of Q-value, and not the convergence trends. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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19 pages, 2140 KiB

Open AccessArticle

Arterial Offset Optimization Considering the Delay and Emission of Platoon: A Case Study in Beijing

by Shenzhen Ding, Xumei Chen, Lei Yu and Xu Wang

Sustainability 2019, 11(14), 3882; https://doi.org/10.3390/su11143882 - 17 Jul 2019

Cited by 10 | Viewed by 2393

Abstract

The effective setting of offsets between intersections on arterial roads can greatly reduce the travel time of vehicles through intersections. However, coordinated control systems of urban arterial roads often do not achieve the desired effect. On the contrary, they are very likely to increase the traffic congestion on arterial roads, resulting in more delays of the platoon with more exhaust emissions, if the coordinated control system does not have effective settings. Meanwhile, taking into account increasing environmental pollution, measures are needed to solve the conflict between environmental and traffic management. Thus, in order to ensure the smooth flow of urban arterial roads while considering the environment, this paper develops a bi-objective offset optimization model, with reducing delays of the platoon on arterial roads as the primary objective, and reducing exhaust emissions as the secondary objective. The proposed bi-objective model is based on the division of platoon operating modes on arterial roads, and more pollutant types, including NOx, HC, and CO, are considered when measuring environmental impact. Further, the modified hierarchical method, combining the branch and bound approach with the introductions of a relaxation coefficient, is employed to solve the model. By introducing a relaxation coefficient, the modified hierarchical method overcomes the defects of the traditional one. Finally, Xi Dajie Road in Beijing was taken as an example. The results showed that the bi-objective offset optimization model, considering both the delays and emissions of the platoon reduced delays by up to 20% and emissions by 7% compared with the existing timing plan. If compared with the offset optimization model considering delays only, such a model increases delays no more than 3% and reduces emissions by 6%. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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19 pages, 498 KiB

Open AccessArticle

Moving Towards Electrification of Workers’ Transportation: Identifying Key Motives for the Adoption of Electric Vans

by Jon Martin Denstadli and Tom Erik Julsrud

Sustainability 2019, 11(14), 3878; https://doi.org/10.3390/su11143878 - 17 Jul 2019

Cited by 7 | Viewed by 2597

Abstract

The large-scale diffusion of low-emission vehicles is required to increase the sustainability of the transport system. Statistics show strong and continued growth in the sales of electric and other low-emission vehicles in the passenger car market. The commercial market, however, has thus far been a different story, despite the fact that vans and other utility vehicles constitute an increasing share of total road traffic and emissions. The present study investigates the potential for increasing the adoption of electric vans (e-vans) among small- and medium-sized enterprises (SMEs). Data gathered in a web survey of 264 SME managers show that 25% of the managers expressed intentions to adopt e-vans within the next two years and another 27% within the next five years. Results from logistic regressions show that a combination of attributes related to the vehicle, the firm and the firm-environment relationships drives adoption intentions. Costs and vehicle reliability are typically important drivers of commercial vehicle purchases. E-vans, however, bring symbolic features into the decision process since they are seen as a measure to improve the green legitimacy of the enterprise. Various measures relevant to manufacturers/dealers and policy makers to stimulate the adoption of e-vans are discussed. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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13 pages, 413 KiB

Open AccessArticle

Environmental Aspects of Generation Y’s Sustainable Mobility

by Michał Suchanek and Agnieszka Szmelter-Jarosz

Sustainability 2019, 11(11), 3204; https://doi.org/10.3390/su11113204 - 08 Jun 2019

Cited by 31 | Viewed by 6210

Abstract

This research paper identifies and explores the opinions and attitudes of young people about urban transport. It is the first study on this topic, based on the survey, analysing the mobility choices of young adults (more specifically, Generation Y) in Poland and for countries in Central and Eastern Europe. The aim of the paper is to show their travel behaviour from sustainable mobility perspective. The primary data was obtained through the online survey. The data analysis was held with use of factor analysis and ANOVA. The research results indicated the variables influencing the environmental dimension of sustainable mobility attitudes of young adults in four areas: the ecology-oriented approach to transport, opinions about sharing economy, public car concept and future transport system. The analysis of variance revealed significant differences in the ecology-oriented approach between people born in different decades, between men and women and between people with driving licences and people without them. Those results provide the insights for local authorities and mobility service providers. The recommendations at the end of the paper focus on the need for continuation of research in similar fields. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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15 pages, 533 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety

by Danish Farooq, Sarbast Moslem and Szabolcs Duleba

Sustainability 2019, 11(11), 3142; https://doi.org/10.3390/su11113142 - 04 Jun 2019

Cited by 42 | Viewed by 5850

Abstract

Driver behavior has been considered as the most influential factor in reducing fatal road accidents and the resulting injuries. Thus, it is important to focus on the significance of driver behavior criteria to solve road safety issues for a sustainable traffic system. The recent study aims to enumerate the most significant driver behavior factors which have a critical impact on road safety. The well-proven Analytic Hierarchy Process (AHP) has been applied for 20 examined driver behavior factors in a three-level hierarchical structure. Linguistic judgment data have been collected from three nominated evaluator groups in order to detect the difference of responses on perceived road safety issues. The comparison scales had been averaged prior to computing the weights of driver behavior factors. The AHP ranking results have revealed that most of the drivers are most concerned about the “Errors”, followed by the “Lapses” for the first level. The highest influential sub-criteria for the second level is the “Aggressive violations” and for the third level, the “Drive with alcohol use”. Kendall’s rank correlation has also been applied to detect the agreement degree among the evaluator groups for each level in the hierarchical structure. The estimated results indicate that road management authorities should focus on high-rank significant driver behavior criteria to solve road safety issues for sustainable traffic safety. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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15 pages, 2536 KiB

Open AccessArticle

Evaluating the Efficacy of Zero-Emission Vehicle Deployment Strategies: The Maryland Case

by Zhenbao Wang, Sevgi Erdogan and Frederick W. Ducca

Sustainability 2019, 11(6), 1750; https://doi.org/10.3390/su11061750 - 22 Mar 2019

Cited by 2 | Viewed by 2606

Abstract

This study aimed to develop a model to estimate the impacts of zero-emission vehicle (ZEV) adoption on CO₂ emissions and to evaluate efficacy of ZEV deployment strategies in achieving greenhouse gas (GHG) emission reduction goals. We proposed a modeling scheme to represent ZEVs in four-step trip-based travel demand models. We then tested six ZEV scenarios that were a cross-combination of three ZEV ownership levels and two ZEV operating cost levels. The proposed modeling scheme and scenarios were implemented on the Maryland Statewide Transportation Model (MSTM) to analyze the impacts of different ZEV ownership and cost combinations on travel patterns and on CO₂ emissions. The main findings were the following: (1) A high-ZEV ownership scenario (43.14% of households with ZEVs) could achieve about a 16% reduction in statewide carbon dioxide equivalent (CO₂Eq) emissions from 2015 base year levels; and (2) CO₂Eq emissions at a future year baseline (2030) (the Constrained Long-Range Plan) level dropped by approximately 11% in low-ZEV ownership scenarios, 17% in medium-ZEV ownership scenarios, and 32% in high-ZEV ownership scenarios. The high-ZEV ownership results also indicated a more balanced distribution of emissions per unit area or per vehicle mile traveled among different counties. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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22 pages, 2164 KiB

Open AccessArticle

Operating Charging Infrastructure in China to Achieve Sustainable Transportation: The Choice between Company-Owned and Franchised Structures

by Lin Ma, Yuefan Zhai and Tian Wu

Sustainability 2019, 11(6), 1549; https://doi.org/10.3390/su11061549 - 14 Mar 2019

Cited by 8 | Viewed by 2747

Abstract

The rapid development of electric vehicles (EVs) is conducive to clean transportation, which is an important aspect of sustainable infrastructure. However, the introduction of EVs is constrained by the lagging development of EV chargers. To optimally promote the development of charging stations, we analyzed the differences in the optimal quality and quantity of EV chargers between company-owned and franchised enterprises by constructing a theoretical model, and the changes in the quality and quantity of EV chargers in different market environments are discussed. We found that the total number of franchised charging stations was larger in general, but that the quality of the franchised charging stations was worse compared with the company-owned stations. The supervision cost, operation cost, and the investment return affect the quality and quantity of EV chargers. Although franchised structures are more conducive in the initial stage to increasing the number of charging stations to meet the needs of EVs, company-owned structures perform better and will be needed to improve the quality of the EV chargers as the market becomes more saturated, necessitating a higher quality of EV chargers. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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16 pages, 1841 KiB

Open AccessArticle

Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

by Jianmin Jia, Chenhui Liu and Tao Wan

Sustainability 2019, 11(3), 643; https://doi.org/10.3390/su11030643 - 26 Jan 2019

Cited by 20 | Viewed by 4035

Abstract

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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15 pages, 1232 KiB

Open AccessArticle

Effects of Perceived Traffic Risks, Noise, and Exhaust Smells on Bicyclist Behaviour: An Economic Evaluation

by Stefan Gössling, Andreas Humpe, Todd Litman and Daniel Metzler

Sustainability 2019, 11(2), 408; https://doi.org/10.3390/su11020408 - 15 Jan 2019

Cited by 33 | Viewed by 5630

Abstract

Active mode (walking, bicycling, and their variants) users are exposed to various negative externalities from motor vehicle traffic, including injury risks, noise, and air pollutants. This directly harms the users of these modes and discourages their use, creating a self-reinforcing cycle of less active travel, more motorized travel, and more harmful effects. These impacts are widely recognized but seldom quantified. This study evaluates these impacts and their consequences by measuring the additional distances that bicyclists travel in order to avoid roads with heavy motor vehicle traffic, based on a sample of German-Austrian bicycle organization members (n = 491), and monetizes the incremental costs. The results indicate that survey respondents cycle an average 6.4% longer distances to avoid traffic impacts, including injury risks, air, and noise pollution. Using standard monetization methods, these detours are estimated to impose private costs of at least €0.24/cycle-km, plus increased external costs when travellers shift from non-motorized to motorized modes. Conventional transport planning tends to overlook these impacts, resulting in overinvestment in roadway expansions and underinvestments in other types of transport improvements, including sidewalks, crosswalks, bikelanes, paths, traffic calming, and speed reductions. These insights should have importance for transport planning and economics. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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15 pages, 5534 KiB

Open AccessArticle

Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

by Hainan Huang, Yi Lin, Jiancheng Weng, Jian Rong and Xiaoming Liu

Sustainability 2018, 10(12), 4725; https://doi.org/10.3390/su10124725 - 11 Dec 2018

Cited by 6 | Viewed by 3521

Abstract

Urban rail transit has become an indispensable option for Beijing residents. Subway inelastic users (SIUs) are the main component among all users. Understanding the proportion of SIUs and their characteristics is important in developing service promotions and helpful for subway agencies in making marketing policies. This paper proposes a novel and simple identification process for identifying regular subway inelastic trips (SITs) in order to distinguish SITs and non-SITs and extract their characteristics. Weekly station sequence (WSS) is selected as the data-based format, principles of SIUs are discussed and chosen, and the framework of SIT identification is applied to a large weekly sample from the Beijing Subway. A revealed preference (RP) survey and results analysis are undertaken to estimate the performance of the proposed methods. The RP survey validation shows that accuracy reaches as high as 94%, and the distribution analysis of SITs and their origin-destinations (ODs) indicate that the SIT characteristics extracted are consistent with the situation in Beijing. The proportion of SIUs is stable on workdays and is more than 80% during rush hour. The efforts described in this paper can provide subway managers with a useful and convenient method to understand the characteristics of subway passengers and the performance of a subway system. Full article

(This article belongs to the Special Issue Sustainable Transportation for Sustainable Cities)

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