Emissions trading is a cost-effective climate policy for reducing greenhouse gas emissions. It could also be useful for addressing road transport emissions, especially given that this sector is the largest CO₂ emitter in the transportation sector and its emissions continue to increase. However, emissions trading for road transport (ETS-RT) has rarely been implemented due to its complexity. This paper designs a novel and practical policy framework for an ETS-RT based on advanced blockchain technology, including all related entities upstream, midstream and downstream of the road transport sector. First, the government determines the cap and allocates the initial permits. Then, fuel producers, vehicle manufacturers, and vehicle users are involved as regulated entities with tradable emission permits. They are responsible for the three determinants of CO₂ emissions in the road transport sector: fuel emission factors, vehicle fuel economy, and vehicle miles travelled, respectively. With all the regulated entities collaborating on compliance, the three determinants can be synergistically optimized so that the efficiency of the emissions abatement can be maximized. In addition, all trading, monitoring, reporting, and verification of the emission permits are automatically executed and recorded via a smart contract deployed on a decentralized blockchain. This approach can dramatically reduce administrative costs, improve transparency and traceability, and eliminate double counting and fraud. Finally, the proposed policy was evaluated using a multicriteria analysis method compared with other possible ETS-RT approaches.

Carsharing is one of the effective ways to relieve the problems of traffic jams, parking difficulties, and air pollution. In recent years, the numbers of carsharing services and their members have remarkably increased around the world. The project of electric carsharing in Shanghai, called EVCARD, has also developed rapidly with very large demand and supply. Aiming to determine the optimal locations of future stations of the EVCARD, this research employs a novel method combining the analytic hierarchy process (AHP) and geographical information system (GIS) with big data. Potential users, potential travel demand, potential travel purposes, and distance from existing stations are selected as the decision criteria. A siting decision system is established, consisting of 15 evaluation indicators which are calculated from multi-source data on mobile phones, taxi trajectory, point of interests (POI), and the EVCARD operation. The method of the AHP is used to determine the indicator weights, and the “Spatial Analyst” tool of ArcGIS is adopted to generate the indicator values for every 1 km × 1 km decision unit. Finally, synthetic scores are calculated to evaluate the candidate sites of EVCARD stations. The results of the case study verify the effectiveness of the proposed method, which can provide a more scientific and feasible method for carsharing operators to site stations, avoiding aimless and random decisions.

In recent years, emissions from the road transportation industry in China have been increasing rapidly. To evaluate the reduction potential of greenhouse gas and pollutant emissions of the industry in China, its emission inventory was calculated and scenario analysis was created for the period between 2012 and 2030 in this paper. Based on the Long-range Energy Alternatives Planning System (LEAP) model, the development of China’s road transportation industry in two scenarios (the business-as-usual (BAU) scenario and the comprehensive-mitigation (CM) scenario) was simulated. In the Comprehensive Mitigation scenario, there are nine various measures which include Fuel Economy Standards, Auto Emission Standards, Energy-saving Technology, Tax Policy, Eco-driving, Logistics Informatization, Vehicle Liquidation, Electric Vehicles, and Alternative Fuels. The cumulative energy and emission reductions of these specific measures were evaluated. Our results demonstrate that China’s road transportation produced 881 million metric tons of CO₂ and emitted 1420 thousand tons of CO, 2150 thousand tons of NO_x, 148 thousand tons of PM₁₀, and 745 thousand tons of HC in 2012. The reduction potential is quite large, and road freight transportation is the key mitigation subsector, accounting for 85%–92% of the total emission. For energy conservation and carbon emission mitigation, logistics informatization is the most effective method, potentially reducing 1.80 billion tons of coal equivalent and 3.83 billion tons of CO₂ from 2012 to 2030. In terms of air pollutant emission mitigation, the auto emission standards measure performs best with respect to NO_x, PM₁₀, and HC emission mitigation, and logistic informatization measure is the best in CO emission reduction. In order to maximize the mitigation potential of China’s road transportation industry, the government needs to implement various measures in a timely and strict fashion.

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.

Owing to the limited‐time relevance and labour cost of traditional traffic service surveys, the quantity and quality of the data collected are not satisfactory in many cases. As more people are becoming active on comment websites and given the advances in natural language processing (NLP) techniques, it is feasible to conduct the opinion mining of comments regarding transportation services at large scale and low cost. In this study, >25,900 comments on websites regarding various public transportation services in Shanghai were analysed using NLP techniques, including sentiment analysis to calculate people's satisfaction with different transit facilities, keyword extraction to understand the key point in comments, and co‐occurrence analysis to understand the commonality and characteristics of different routes. The sentiment analysis shows that people are more satisfied with traffic hubs than vehicles, and people have the highest level of satisfaction with airports and the lowest level of satisfaction with buses. The keywords classified by sentiment values show the strengths and weaknesses of Bus No. 49. Finally, the authors interpret a few anomalies of the results by tracing the context around the keywords. This study designs an NLP approach of transportation survey to improve the quality of public transportation service.

Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.