As more cities adopt bike share systems, questions regarding equity, accessibility, expansion, and bikability arise. Baltimore City implemented bike share in 2016 with plans to expand it up to 57 stations. This paper introduces a new methodology for an equity-based planning analysis which help minimize segregation and marginalization in planning practices. The proposed geographic methodology incorporates a modified population density-based bike equity index and develops a level of traffic stress index to prioritize bikeshare infrastructure. This study provides a parallel prioritization scheme for implementing bikeshare and supporting cycling infrastructure.

Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and is mostly available as a special service because of its high operating costs due to intensive labor costs as well as requirement for advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, so preparing for such automated flexible feeder services is necessary to take advantage of the rapid improvement of automated vehicle technology. Therefore, in this research, an algorithm for optimal flexible feeder bus routing, which considers relocation of buses for multiple stations and trains, was developed using a simulated annealing algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocation of buses when the optimal bus routings were not feasible using the buses available at certain stations. Furthermore, the developed algorithm limited the maximum degree of circuity for each passenger while minimizing the total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs.

Advanced safety features in vehicles are emerging features that car manufacturers design and produce to improve the safety of driving a vehicle. In recent years, car manufacturers increasingly have provided different safety features as options to their customers. Understanding consumers’ interests and priorities relating to these features in vehicles has motivated researchers to conduct market analysis studies. This study aims to analyze the willingness to pay (WTP) for the advanced safety features in vehicles by adults resident in the U.S.A. Implementing adaptive choice-based conjoint analysis (ACBC), which is a special market analysis technique, and using comprehensible and realistic attributes in the survey are the main innovations of this study. The results of this study show that surround monitoring and collision avoidance systems are the most important attributes for respondents in their next vehicle. After that, the vehicle’s connectivity system was the third most important factor. Respondents distinctly preferred a vehicle with fully automated crash avoidance features. Moreover, the vehicle with safety features having the highest utilities may capture almost 61% of the market.

Driving simulators can provide safe real-world driving conditions that may help researchers study driving behavior; however, driving simulator sickness (DSS) has been recognized as one of the most frequent challenges associated with using driving simulators. The DSS issue may affect the validity and reliability of results obtained during the driving simulator experience. Therefore, it is necessary to understand the potential consequences of DSS in the virtual environment of a driving simulator. The goal of this study is to analyze DSS symptoms among participants of a driving simulator by their demographics. The samples of four simulator studies were combined, including a total of 259 participants who were recruited from different socio-demographic backgrounds and drove a fixed-base driving simulator. All these studies used the same proportion of mixed urban and suburban content in designing the simulation environments of the experiments. A DSS questionnaire based on the standard Simulator Sickness Questionnaire (SSQ) has been used to measure the severity of DSS symptoms—general discomfort, fatigue, headache, eyestrain, blurred vision, salivation, sweating, dizziness, and nausea—among participants. This study applies zero-inflated ordered probit and ordered probit models to evaluate the possible statistical relationships between demographic characteristics and experiment duration, and DSS symptoms. The results show that there is a positive direct statistical relationship between the duration of the experiment and DSS. Also, older participants have more general discomfort, fatigue, blurred vision, and headache symptoms with DSS than do younger ones. Similarly, female participants experience headache and nausea symptoms more than men do.

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.