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Many cities across the world are looking to use technology and innovation to improve the overall efficiency and safety for their residents. At the heart of these smart-city plans, a variety of intelligent transportation system technologies can be used to improve safety, enhance mobility measures (e.g., traffic flow), and minimize environmental impacts of a city’s mobility ecosystem. Early implementations of these ITS technologies often take place in affluent cities, where there are many funding opportunities and suitable areas for deployment. However, it is critical that we also develop smart city solutions that are focused on improving conditions of disadvantaged and environmental justice communities, whose residents have suffered the most from unmitigated urban sprawl and its environmental and health impacts. As a leading example, Inland Southern California has grown to be one of the largest hubs of goods movement in the world. Numerous logistics facilities such as warehouses, rail facilities, and truck depots have rapidly spread throughout these communities, with the local residents bearing a disproportionate burden of truck traffic, poor air quality, and adverse health effects. Further, the majority of residents have lower-wage jobs and very few mobility options, other than low-end personal car ownership. To improve this situation, UC Riverside researchers have focused their smart city research on these impacted communities, finding innovative solutions to eco-friendly traffic management, developing better-shared (electric) mobility solutions for the community, improving freight movements, and enhancing the transition to vehicle electrification. Numerous research and development projects are currently underway in Inland Southern California, spanning advanced smart city modeling and impact analysis, community outreach events, and real-world technology demonstrations. This chapter describes several of these ITS solutions and their potential for improving many cities around the world. 

Connected vehicle-based eco-driving applications have emerged as effective tools for improving energy efficiency and environmental sustainability in the transportation system. Previous research mainly focused on vehicle-level or link-level technology development and assessment using real-world field tests or traffic microsimulation models. There is still high uncertainty in understanding and predicting the impact of these connected eco-driving applications when they are implemented on a large scale. In this paper, a computationally efficient and practically feasible methodology is proposed to estimate the potential energy savings from one eco-driving application for heavy-duty trucks named Eco-Approach and Departure (EAD). The proposed methodology enables corridor-level or road network–level energy saving estimates using only road length, speed limit, and travel time at each intersection as inputs. This technique was validated using EAD performance data from traffic microsimulation models of four trucking corridors in Carson, California; the estimates of energy savings using the proposed methodology were around 1% average error. The validated models were subsequently applied to estimate potential energy savings from EAD along truck routes in Carson. The results show that the potential energy savings vary by corridor, ranging from 1% to 25% with an average of 14%. 

The boom of e-commerce and the increasing demand for fast and reliable delivery services have led to the thriving of on-demand delivery (ODD), which provides delivery services to food takeout, grocery, pharmacy, and other light-weighted goods. The operational efficiency of ODD is subject to many factors—access to curbside, delays at the pick-up and drop-off locations, order dispatching mode, vehicle routing schedule, and vehicle refueling needs. The fast-growing delivery orders coupled with operational inefficiencies of ODD may lead to higher vehicle miles traveled (VMT) and pollutant emissions. Policymakers as well as practitioners need to evaluate the VMT and emissions impact of ODD, given the consumer behavior, operational paradigm, and business models. This paper conducted a systematic review of the existing literature to synthesize and summarize the impacts of ODD with a specific focus on VMT and emissions. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline was employed to systematically search for related studies in multiple databases and to crystallize the review scope. The impact evaluation was delved into three aspects: customer shopping behavior (online shopping vs. in-store shopping), ODD operational strategy (truck/van vs. green vehicles, professional delivery vs. crowdsourcing), and business models (home delivery vs. depot/collection point). Overall, this study found that online shopping with coordinated ODD can achieve significant VMT and emissions reduction compared with in-store shopping. The reduction extent depends on the customer trip chaining, travel mode choice, residential area type, and the ratio of product return. The use of zero-emissions vehicles in ODD, such as electric van/truck/vehicle, cargo-bike, UAV, provides relatively higher emissions reductions, but also brings new issues such as charging needs or capacity limits. Collection points (e.g., parcel locker, retailer store, postal service point) can reduce the VMT and emissions if they are optimally distributed, and customers visit them in zero-emissions modes or through trip chaining. 

Electric Vehicle (EV) charging has been a significant barrier to the widespread use of EVs. Traditional EV charging methods depend on cables, and there are concerns about safety, accessibility, convenience, and weather. A recent development, dynamic (or in-motion) wireless charging, enables EVs to charge wirelessly by incorporating charging infrastructure into roadways, allowing EVs to charge while moving. However, the energy transferred relies heavily on vehicle speed and time spent in the charging lane. This paper proposes an innovative solution that combines dynamic wire-less charging with Variable Speed Limit (VSL) control. This dynamic traffic control strategy adjusts speed limits based on real-time traffic, weather, and incidents. This integration of dynamic wireless charging and VSL has two potential benefits. First, it can motivate driver compliance with VSL through the incentive of charging. Second, it can promote smoother traffic flow and improve traffic safety by implementing lower speed limits at certain times. To verify these benefits, microscopic traffic simulations in SUMO were conducted under different EV penetration rates and VSL compliance rates. Simulation results reveal that the proposed approach can enhance dynamic wireless charging system performance while improving traffic flow and safety 

Electrifying the ride-hailing services has the potential to significantly reduce greenhouse gas emissions in the shared mobility sector. However, these emission reduction benefits depend on the utilization of EVs to serve trip requests, especially during the fleet electrification process. In this paper, we evaluated the performance and emission impacts of ride-hailing service with three dispatching policies and various EV penetration levels in the ride-hailing fleet. A large-scale simulation platform was developed for the city of San Francisco in SUMO to enable the application of ride-hailing, electric vehicle charging, and idle vehicle repositioning. Simulation results indicate that with a 60% EVs in the simulated fleet, the off-peak EV priority policy and off-peak EV only policy can reduce CO2 emissions by 32% - 40% while preserving the mobility performance in terms of deadheading, total travel distance, and average rider pick-up time. It is important for ride-hailing platforms to increase the zero-emission rides and encourage ride pooling to comply with California’s Clean Miles Standard. 

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC. 

Urban air quality and the impact of mobile source pollutants on human health are of increasing concern in transportation studies. Existing research often focuses on reducing traffic congestion and carbon footprints, but there's a notable gap in understanding the health impacts of traffic from an environmentally-just perspective. Addressing this, our paper introduces an integrated simulation platform that models not only traffic-related air quality but also the direct health implications at a microscopic level. This platform integrates five modules: SUMO for traffic modeling, MOVES for emissions modeling, a 3D grid-based dispersion model, a Matlab-based visualizer for pollutant concentrations, and a human exposure model. We emphasize the transportation-health pathway, examining how different mobility strategies impact human health. Our case study on multimodal on-demand services demonstrates that a distributed pickup strategy can reduce cancer risk from PM 2 . 5 exposure by 33.4% compared to centralized pickup. This platform offers insights into traffic-related air quality and health impacts, providing valuable data for improving transportation systems and strategies with a focus on health outcomes. 

Exclusive bus lane strategy is widely adopted in many cities to improve bus operation effciency and reliability. With the development of connected vehicle technologies, the dynamic bus lane (DBL) strategy was proposed, with allowing general vehicles to share use of the bus lane to improve traffc effciency in general purpose lanes (GPLs). Previous studies have rarely considered the eco-driving strategy of connected and automated vehicles/buses (CAVs/CABs) in GPLs under the mixed traffc conditions, and how to ensure bus priority with DBL control. In this study, a novel DBL control strategy was developed under the partially connected vehicle environment. A trajectory planning method while considering the joint effects of bus stop and signal phase for CAB was adopted, an eco-driving strategy for CAVs in GPL was proposed using a trigonometry trajectory planning method. And a novel DBL control method was established by integrated trajectory planning for both the CAVs and CABs to ensure bus operation priority. Numerical experiments were conducted to evaluate performance of the proposed novel DBL control in terms of travel time and energy consumption of general vehicles at the different levels of CAV market penetration rates (MPRs). Results indicated that about 16%-42% energy savings can be achieved with MPR varying from 20% to 100%, and the travel time can be improved by about 4%-10%. Meanwhile, sensitivity analysis was conducted to quantify the impacts of key parameters, including vehicle target speeds, heterogeneous traffc fow, random arrival interval of cars, position of bus stop, traffc volume in GPL 

The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed. 

The emerging prevalence of electric vehicles (EVs) in shared mobility services has led to a groundbreaking trend for decarbonizing the shared mobility sector. However, it is still unclear how to maximize the efficiency of EVs to reduce greenhouse gas (GHG) emissions while maintaining high service quality, particularly considering the ongoing transition towards a fully electrified service fleet. In this paper, focusing on meal delivery, we proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of EVs to mitigate GHG emissions and maintain low operational cost and delay cost. The main feature of our system is that its fleet consists of electric and gasoline vehicles mirroring the evolving electrification trend in the shared delivery sector. A rolling horizon framework integrated with the adaptive large neighborhood search (RHALNS) algorithm was proposed to efficiently solve the meal order dispatching and routing problem with the mixed fleet. Three delivery policies were explored in the numerical study. Experiment results demonstrated that it is necessary for online meal delivery platforms to actively collect information of electric vehicles and take initiative to employ an eco-friendly delivery policy.