Road project scheduling and contract bundling are two separate concepts that profoundly influence project delivery consequences to key stakeholders. Therefore, agencies espouse initiatives for contract bundling (to reduce project costs) and project scheduling (to reduce user delay costs). Bundling combined with scheduling could potentially yield benefits to both the agency and urban road users. This paper investigates the overall benefits (to the two stakeholders) of optimal schedules with bundling considerations, compared to the optimal schedules for unbundled contracts. The agency's objective is to make scheduling‐with‐bundling decisions that minimize system‐wide travel time and project costs, and the users’ objective is to minimize their travel times in the network with work zones. The problem is solved using the non‐dominated sorting genetic algorithm. The numerical experiments suggest that with well‐designed bundling, optimal scheduling can significantly reduce not only the project cost but also the total system cost (project and travel delay).

Metropolitan authorities continue to seek programs and initiatives to reduce emissions in their jurisdictions. It has been shown that transitioning from fossil fuel to electric propulsion of transportation can help realize this goal. However, the current market penetration of electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) remains very small. This paper proposes a framework to address this problem over a long-term analysis period. The paper accounts for consumers' vehicle-purchasing propensities and their route choices, locations of EV-charging and ICEVrefueling stations. In the proposed framework, new EV charging stations are provided at selected locations and/or existing gas stations are repurposed by the transport agency's decisionmaker (through policy) in conjunction with the private sector (through investment). The paper presents a bi-level mathematical model to capture the decision-making processes of the transport agency and the travelers. Underlying the framework is a solid theoretical foundation for the EV charging network design. The design problem is solved using an active-set algorithm. The study results can serve as guidance for metropolitan transport agencies to establish specific locations and capacities for EV stations and thereby to contribute to longterm reduction of emissions.

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A key challenge facing cities of today is the persistent and growing urban congestion that has significant adverse effects on economic productivity, emissions, driver frustration, and quality of life. The concept of smart cities, which can revolutionize the management of metropolitan transportation operations and infrastructure, shows great promise in mitigating this problem. Specifically, the automation and connectedness (A&C) of smart city entities such as its infrastructure, services, and vehicles, can be helpful. In this regard, this paper focuses on the potential of autonomous vehicles (AVs) and AV infrastructure, particularly during prospective transition era where there will be mixed streams of AVs and human driven vehicles (HDVs). The paper considers two aspects of this potential: connectivity-enabled travel demand management and travel infrastructure supply through lane management. To demonstrate the opportunity associated with this potential, this paper first presents an AV-enabled tradable credit scheme (TCS) to manage travel demand. Here, the transportation authority distributes travel credits to travelers directly and instantaneously using the AV's A&C features. Travelers use their A&C features to pay these credits for travel at specific locations or times-of-day according to their choices of lane types and links. With regard to supply, this paper considers that the road network consists of two lane types: AV-dedicated, and mixed traffic lanes, and develops a scheme for travel demand and lane management strategies in the AV transition era (TLMAV). Firstly, the paper models the expected travel choices based on user equilibrium concepts, at different levels of AV market penetration. Then, the existence of the optimal solution in terms of link flows and the prevailing travel credit price is demonstrated. Then the paper establishes the optimal TLMAV that minimizes total travel time subject to user equity constraints. The results demonstrate the extent to which HDV users will suffer an increase in travel cost if equity is not considered in the model. The results also show how the transportation agency can use TLMAV to keep HDV travel costs to acceptable levels, particularly during the early stages of the AV transition period. Further, the paper shows how TLMAV could be designed to gradually diminish inequity effects so that travelers, in the long term, are motivated to shift to AVs.

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