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Accurate detection of Vulnerable Road Users (VRUs) by roadside sensors poses significant challenges due to sparse labeled data and multi-modal sensor fusion complexity. This paper proposes a hierarchical multi-modal fusion framework that integrates traditional 3D object detection methods with deep learning-based approaches. Our method combines the robustness of conventional detection pipelines with neural network adaptability through a novel complementarity strategy. The framework employs DAB-DETR for 2D detection, traditional 3D clustering algorithms, and PointPillars architecture, unified through confidence-based fusion. Experimental evaluation on the Intersection Safety Challenge (ISC) dataset demonstrates the effectiveness of our approach with overall mAP@0.5 of 49.12% compared to 36% for conventional methods and 15.99% for deep learning approaches. Notable improvements include vehicle detection (46.09% vs. 39.12%) and VRU detection, especially for children (30.71% vs. 3.61%). Our hierarchical fusion strategy successfully leverages complementary strengths of traditional classification accuracy and deep learning localization precision. The solution based on this detection method won an award in the ISC Stage 1B competition, validating its effectiveness for data-constrained roadside VRU detection scenarios. 

Most existing signal control systems utilize fixed-location sensors (e.g., loop detectors or roadside cameras), which are constrained by limited spatial coverage and have relatively high installation and maintenance costs. As an alternative method for vehicle detection and traffic management at signalized intersections, it is possible to use vehicle-probe data, collected from smartphones, navigational aids, GNSS receivers, and other types of mobile devices. In this paper, we propose a cost-effective vehicle-probe-based signal management technology to leverage the widely available probe data for traffic signal control. To demonstrate its effectiveness, we then develop a microscopic simulation to compare its performance to a state-of-the-practice system. A candidate “unbalanced” intersection is identified in the City of Riverside and replicated in a microscopic simulation. The performance of the vehicle-probe-based optimized signal control plan is evaluated under the calibrated traffic demand. The simulation results demonstrated that it significantly reduces the length of the eastbound peak-hour queue from 800 m to under 50 m. The average travel time was reduced by 69% for the eastbound and 40% for the westbound, while maintaining a similar level in the northbound and southbound. The vehicles’ emissions were decreased by 32%–52% due to the mitigation of congestion in the major westbound-eastbound direction, resulting in fewer vehicles being halted in queues. Overall, the proposed vehicle-probe-based signal management technology demonstrated significant potential in enhancing traffic efficiency and reducing environmental impact. It provides a low-cost, scalable, and sustainable solution that can be potentially applied to both fixed-time and adaptive signalized intersections. 

Connected and automated trucks (CATs) have the potential to transform the transportation system and logistics industry. Their unique features, such as operational strategies and truck driving behaviors, can affect transportation system performance. For successful development, testing and deployment of CATs, analysis, modeling, and simulation (AMS) plays an important role, especially in evaluating the impacts of CAT technologies on existing transportation systems. This paper presents a comprehensive review and assessment of up-to-date studies related to CAT AMS, focusing on three correlated elements: CAT applications, data, and tools. The research delves into CAT applications from individual CAT and CAT fleet to CAT-involved traffic. It explores available data sources relevant to CAT system use cases, assessing their potential issues and opportunities. The study also reviews existing AMS tools used to analyze CAT applications at both operational performance and network integration levels, emphasizing research needs in CAT-specific tools development. The findings identify the data needs and point out that existing AMS tools may not capture the complexity of CAT operation, which involves driving behaviors, vehicle-to-everything communications, autonomous capabilities, and response to truck-specific scenarios. The study will lay a solid foundation for further development of the AMS framework for CATs and provide guidance to future research of CAT applications. 

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. 

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. 

With the rapid growth in e-commerce in the last few decades, the amount of truck traffic, especially in urban areas, has increased substantially. The emergence of connected and automated vehicle technology has enabled the development of various advanced applications aimed at improving the safety, efficiency, and sustainability of existing transportation systems to support the increased traffic. As an example, freight signal priority (FSP) and Connected Eco-Driving (CED) applications have been proposed to reduce vehicle stops and delays at signalized intersections, thereby enabling more efficient travel on urban corridors. However, the majority of FSP and CED studies to date were conducted in simulation due to the limited availability of connected vehicles and infrastructure in the real world. In this study, we implemented these two applications on an adaptive coordinated signalized corridor in Long Beach, California, and evaluated their efficacy with a connected truck. The field trials demonstrated that both FSP and CED effectively smoothed the trajectories of the connected truck, leading to significant reductions in fuel consumption and travel time. These findings confirm the potential of these two connected vehicle applications for enhancing the efficiency and sustainability of urban freight operations. 

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.