Search for: All records

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. 

The market for on-demand food delivery (ODFD) has increased considerably, especially during the COVID-19 pandemic. It is crucial for transportation and environmental agencies to understand how ODFD has reshaped the travel patterns of people, affecting vehicle-miles traveled (VMT) as well as pollutant emissions in the transportation system. However, the lack of public data from food delivery companies makes it challenging to quantify the impact of on-demand delivery on the real-world transportation network. In this research, we propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips. Both short-term and long-term impacts of the COVID-19 pandemic are evaluated. Experimental results show that ODFD has great potential to reduce the dining-related VMT and emissions. The total dining-related VMT in the during-pandemic case decreased by 38% and in the after-pandemic case reduced by 6% to 9%, and the corresponding environmental impacts were reduced accordingly. Meanwhile, emissions reduced significantly with more electric vehicles involved in food delivery. With 100% electric delivery fleet, the ODFD service can save 14% to 22% of emissions after the COVID-19 pandemic.