Persons with disabilities have difficulties traveling from one point to the other due to the limited options of travel modes for the first and last mile. Western Michigan University tested using an autonomous shuttle on the main campus's sidewalks for persons with disabilities. This study's objectives are to understand the empathy college students without disabilities had on the need for suitable transportation services for students with disabilities and the perceived risks of the services' operation on sidewalks. The Bayesian ordered logit model and text mining analyzed 396 survey responses. The Bayesian ordered logistic regression results revealed that age, gender, and ethnicity are important factors that contribute to different opinions concerning perceived risks and sympathy brought by an autonomous shuttle operating on pedestrians' sidewalks. The text mining results revealed several patterns. While respondents who were against the operation focused on potential safety hazards and the crowdedness of the sidewalks, supporters focused on the expected improved mobility for people with disabilities. The findings from this study are expected to assist policymakers and vehicle manufacturers with pedestrian expectations and considerations related to risk and safety when sharing their walkways with the autonomous shuttle.

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans. This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume. First, potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects. Second, the principal component analysis (PCA) is applied to identify key factors that impact the airline passenger volume of city pairs. Then the panel data model is estimated using 120 sets of data, which are a collection of observations for multiple subjects at multiple instances. Finally, the airline data from Chongqing to Shanghai, from 2003 to 2012, was used as a test case to verify the validity of the prediction model. Results show that railway and highway transportation assumed a certain proportion of passenger volumes, and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume. According to the validity test results, the prediction accuracies of the model for 10 sets of data are all greater than 90%. The model performs better than a multivariate regression model, thus assisting airport operators decide which routes to adjust and which new routes to introduce.

With the increasing severity of urban traffic congestion and environmental pollution issues, Mobility-as-a-Service (MaaS) has garnered increasing attention as an emerging mode of transportation. Thus, how to motivate users to participate in MaaS has become an important research issue. This study first classified the incentive policies into four aspects: financial incentive policy, non-financial incentive policy, information policy, and convenience policy. Then, through online questionnaires and field interviews, 456 sets of data were collected in Beijing, and the data were analyzed by the structural equation model and latent class model. The results show that the four incentive policies are positively correlated with users' participation in MaaS, among which financial incentive policy and information policy have the greatest impact, that is, they can better encourage users by increasing direct financial subsidies and broadening the information about MaaS. In addition, Latent Class Analysis was performed to class different users and it was found that the personal characteristics of users had some influence on willingness to participate in MaaS. Therefore, incentive policies should be designed to consider the needs and characteristics of different user groups to improve their willingness to participate in MaaS. The results can provide theoretical suggestions for the government to promote the widespread application of MaaS in urban transportation.

Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians. Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents. Faced with the complex issue of pedestrian occlusion at signal-free intersections, this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN (HAERC). Specifically, the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target. Additionally, to address the misselection caused by overlapping candidate boxes in two-stage target detection models, an Extended Non-Maximum Suppression classifier (ENMS) with expanded IoU thresholds is proposed. Finally, leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels (heavy, reasonable, partial, bare), the HAERC model is experimented on these classes and compared with baseline models. Experimental results demonstrate that HAERC achieves superior False Positives Per Image (FPPI) values of 46.64%, 9.59%, 9.43%, and 6.78% respectively for the four classes, outperforming all baseline models. The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals, thereby enhancing safety for long-range driving at such intersections.