Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.

Accurate and effective prediction of future traffic accident risk is critical to reducing the number of traffic accidents, which is also of great help to personal safe travel. In our paper, we choose the real traffic administrative area as the way of regional division rather than grid map, so that our prediction results can be applied to true traffic scenarios directly. Instead of considering traffic flow as a single factor affecting traffic accidents, we divide traffic flow into multiple traffic volumes based on vehicle type. In order to better model the dynamic impact of different traffic flow data and traffic accident data in the local region and global regions for future traffic accident risk prediction, we design a deep learning framework to predict regional Traffic Accident risk that utilizes a Spatial-Temporal Attention Network (named TA-STAN). We also integrate many external environmental factors to further improve the accuracy. We evaluate our TA-STAN model on the real traffic accident dataset in New York City. The experimental results show that TA-STAN outperforms 6 baseline models in 3 evaluation metrics. More importantly, by visualizing the weight of attention, we can reasonably interpret the actual meaning of attention weights, which plays a crucial role in our model.

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated "Drive Me" project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

Most existing shared automated mobility (SAM) services assume the door-to-door manner, i.e., the pickup and drop-off (PUDO) locations are the places requested by the customers (or demand-side). While some mobility services offer more affordable riding costs in exchange for a little walking effort from customers, their rationales and induced impacts (in terms of mobility and sustainability) from the system perspective are not clear. This study proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework, aiming to fill this knowledge gap and to assess the mobility and sustainability impacts. The optimal ride matching problem is formulated and solved in an online manner through a micro-simulation model, Simulation of Urban Mobility (SUMO). The objective is to maximize the profit (considering both the revenue and cost) of the proposed SAM service, considering the constraints in seat capacities of shared automated vehicles (SAVs) and comfortable walking distance from the perspective of customers. A case study on a portion of a New York City (NYC) network with a pre-defined fleet size demonstrated the efficacy and promise of the proposed system. The results show that the proposed DC-SAM service can not only significantly reduce the SAV’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can also considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model. In addition, the demand-side cooperation strategy can bring about additional system-wide mobility and sustainability benefits in the range of 4–10%.