Shared autonomous vehicles (SAVs) are rapidly emerging as a viable alternative form of public transportation with the potential to provide adequate and user-friendly, on-demand services without having vehicle ownership. It has been argued that SAVs could revolutionize transportation systems and our current way of life. Although SAVs are likely to be introduced in developed countries first, there is little doubt that they would also have a significant effect and enormous market in developing nations. This study aimed to investigate the factors that influence public acceptance of SAVs, as well as the current public attitude toward SAVs, in two developing countries, namely, Pakistan and China. A stated preference survey was conducted to understand respondents’ travel patterns, preferences, and sociodemographic data. A total of 910 valid responses were gathered: 551 from Lahore, Pakistan, and 359 from Dalian, China. A multinomial logit model and a mixed multinomial logit model with panel effect were used for data analysis. The results suggested that generic attributes, such as respondents’ waiting time, travel time, and travel cost were found to be significant in both cities. The results indicate that sociodemographic characteristics, such as education, income, travel frequency in a week, and people who had driver’s licenses, are significantly correlated with respondents’ interest in using SAV in Lahore. The results also showed that people who had a private car indicated a greater interest in SAVs in Dalian. The study provides a new perspective to understand the public preferences toward SAVs in developing countries with different economies and cultures, as well as a benchmark for policymakers to make effective policies for the future implementation of SAVs.

Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.

As the parcel delivery service is booming in China, the competition among express companies intensifies. This paper employed multinomial logit model (MNL) and latent class model (LCM) to investigate customers’ express service choice behavior, using data from a SP survey. The attributes and attribute levels that matter most to express customers are identified. Meanwhile, the customers are divided into two segments (penny pincher segment and high-end segment) characterized by their taste heterogeneity. The results indicate that the LCM performs statistically better than MNL in our sample. Therefore, more attention should be paid to the taste heterogeneity, especially for further academic and policy research in freight choice behavior.

Level of Service (LOS) in transportation refers to the traveler's perception of the quality of transportation service achieved from the traffic condition and the facility. A fundamental problem in traditional LOS evaluations for a transportation system is that it is always auto-centric. This means that the LOS for automobiles is always considered above other travel modes. In the new era of green transportation, there is an urgent need for a new LOS evaluation method so that other modes - including bus, light rail, bike and pedestrians - can also be evaluated, thus providing a more comprehensive assessment of the facility. The evaluation of LOS should focus on `travelers' instead of `drivers'. Furthermore, current LOS measures may not adequately reflect LOS as perceived by travelers. Focusing on these two issues, a new utility-based method to measure the urban transportation system LOS is proposed. The utility theory and user equilibrium model are used to establish and solve the problem. The proposed multimodal LOS measure is based on the aggregated utility of all travelers in the transportation system, which is estimated by a traveler's utility function that considers impact factors such as travel time and cost. The objective of this paper is to develop a model to calculate the threshold or maximum value of the LOS categories, which is determined based on total utility of the travelers in the transportation system. Results verify the feasibility of using the proposed measure for multimodal LOS evaluation. The paper concludes with an example and analysis.

Bus rapid transit (BRT) is characterized by higher speed, higher comfort level, and larger capacity than conventional bus service. Although many cities worldwide have adopted BRT in recent years, there is an absence of scientific and quantitative approach for BRT network planning. The problem of BRT planning in an existing transportation network is very complex with constraints of road geometrics, regulations, right of way, travel demand, vehicle operations, and so on. This paper focuses on developing an optimization model for BRT network planning, where an integer programing model is established to identify station locations and route layout with the objective of maximizing the number of trips served by the network, subjected to constraints including distance between stations, cost of construction, road geometrics, etc. The detour factor of the BRT route, which is an important indicator but widely ignored in previous studies, is also taken as a constraint. A heuristic method is applied to generate optimal solutions to the integer programming model, followed by a case study using the transportation network and travel demand data in Luoyang, China. The numerical results show that the method is valid and can therefore be applied to improve BRT network planning and the sustainable transportation system development.