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This paper presents the potential human health impacts from connected and autonomous vehicles (CAVs) in the San Francisco, Bay Area, U.S. The study concentrates on impacts derived from CAVs’ outcomes on travel demand, safety, and environmental emissions. The paper combines travel modeling practices, critical literature review, and the authors’ expert inference, to quantify the human health impacts using the Integrated Transport and Health Impacts Model (ITHIM). Specifically, ITHIM estimates impact considering changes in travel demand, level of physical activity, emissions, and safety features. This study estimates a 10% increase in car travel mileage and an 11% decrease in walk/bike trip mode share at the presence of CAVs; a 70%–90% crash reduction because of safer CAVs; and significant emission reduction by several CAV-enabled mechanisms such as eco-driving (over 30% CO2 emission reduction) and engine performance adjustment (over 20% CO2 emission reduction). The health impact assessments show significant opportunities for road traffic injury reductions (annual saving of more than 300 deaths and 14,000 life years). However, reduced physical activity because of the mode shift to the car could cause negative health outcomes (increased annual deaths and disability-adjusted life years (DALYs) by more than 30–60 and 700–900, respectively). A set of (active transport [AT] and vehicle electrification) scenarios were developed that could mitigate the potential health-related risks and evaluated their effectiveness. The results show enormous benefits from 50% increased travel-related physical activity which offset the CAV health drawbacks and generates additional benefits by preventing premature deaths and saving life years.

This paper contributes to the emerging body of research on crowdshipping, which is a collaborative strategy that distributes delivery tasks to a mass of actors that act as ordinary couriers, aiming at reducing delivery costs and supporting sustainability. The study focuses on last mile delivery activities, where numerous app-based delivery platforms have recently emerged. The paper provides an overview of the operational characteristics of these platforms based on an in-depth study of a sample of major crowdshipping services (state-of-practice) and a comprehensive review of the state-of-research. After comparing platform services and characteristics, we identified four core (typological) factors that differentiate services: platform (service) type, delivery type, delivery mode, and pricing strategy; and six categories for service challenges and opportunities. Moreover, the review of the state-of-research synthesized their findings with respect to the identified practical challenges to discover opportunities for future work. Overall, the study found that there is a mismatch between practical challenges and scientific solutions. The literature has not addressed all challenges identified in practice, such as couriers’ work conditions and pricing, which are still unresolved issues. The majority of articles were exploratory in nature with their findings based on hypothetical random instances. More research is needed with empirical case studies to evaluate the service net effects on each actor (e.g., senders, receivers, couriers, and platforms) in particular, and on the society in general, in terms of traffic externalities, quality of life, cost and revenue. The paper ends with a discussion of promising areas for future research.

Evacuation planning is an important activity in disaster management. Due to the sudden occurrence of disasters, evacuation should be preplanned. It is necessary for evacuation plan to be as close as possible to a real evacuation operation. However, evacuation planning is challenging because of inherent uncertainty in required information. One important source of uncertainty in evacuation is the uncertainty in evacuation demands. This paper presents an evacuation vehicle routing problem to design evacuation routes for public vehicles. In this problem, demand (number of evacuees) at each pick-up point is introduced as a fuzzy number and the assignment of the pick-up points to the vehicle routes happens based on a credibility preference index. A genetic algorithm based on fuzzy credibility theory is designed to optimize the problem. The optimum parameter set for genetic algorithm is obtained by Taguchi experimental design. The presented problem and algorithm are applied on a part of Tehran transportation network. The impact of credibility preference index on the achieved solutions is evaluated and its best value is determined for the case study. Moreover, different tests are implemented to analyze and discuss the influence of uncertainty level on the final results.

This study proposes an earthquake evacuation routing plan from local shelters to regional ones for a long-term safe settlement using public vehicles. In a post-earthquake situation, the unpredicted changes in travel demand patterns and accessibility conditions of the transportation network affect the travel time. The contribution of this study is to propose a dynamic evacuation routing approach that can update the routing plan by incorporating time-dependent travel times. The problem is modeled as a vehicle routing problem and a two-stage solution procedure based on the simulated annealing algorithm is developed. The model is applied in part of Tehran’s transportation network. The results confirm that the dynamic evacuation routing approach is able to increase the number of evacuated shelters and decrease both the evacuation time and total travel time of the vehicles. The findings in this study indicate that the application of the proposed model can provide beneficial information for disaster mana...