| A hybrid deep learning based traffic flow prediction method and its understanding |
84 |
| What have we learned? A review of stated preference and choice studies on autonomous vehicles |
52 |
| The roles of initial trust and perceived risk in public's acceptance of automated vehicles |
52 |
| DeepPF: A deep learning based architecture for metro passenger flow prediction |
48 |
| What drives people to accept automated vehicles? Findings from a field experiment |
45 |
| Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments |
45 |
| Autonomous vehicle perception: The technology of today and tomorrow |
44 |
| An empirical investigation on consumers' intentions towards autonomous driving |
43 |
| On the min-cost Traveling Salesman Problem with Drone |
42 |
| A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation |
39 |
| Inferring transportation modes from GPS trajectories using a convolutional neural network |
37 |
| A deep learning approach for detecting traffic accidents from social media data |
37 |
| Dynamic autonomous vehicle fleet operations: Optimization-based strategies to assign AVs to immediate traveler demand requests |
36 |
| An optimal charging station location model with the consideration of electric vehicle's driving range |
33 |
| The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets |
32 |
| A modeling framework for the dynamic management of free-floating bike-sharing systems |
31 |
| Predicting station-level hourly demand in a large-scale bike sharing network: A graph convolutional neural network approach |
31 |
| Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios |
30 |
| Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations |
30 |
| Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions |
30 |
| Recent applications of big data analytics in railway transportation systems: A survey |
29 |
| Energy saving potentials of connected and automated vehicles |
29 |
| Human-like autonomous car-following model with deep reinforcement learning |
28 |
| Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming |
27 |
| Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors |
27 |
| Enhancing transportation systems via deep learning: A survey |
27 |
| Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach |
26 |
| A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation |
25 |
| A range-restricted recharging station coverage model for drone delivery service planning |
24 |
| Modeling impacts of Cooperative Adaptive Cruise Control on mixed traffic flow in multi-lane freeway facilities |
24 |
| A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection |
24 |
| Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes |
23 |
| An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones |
23 |
| Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors |
23 |
| An empirical study on travel patterns of internet based ride-sharing |
23 |
| A human-like game theory-based controller for automatic lane changing |
23 |
| Experimental validation of connected automated vehicle design among human-driven vehicles |
22 |
| Using structural topic modeling to identify latent topics and trends in aviation incident reports |
22 |
| Urban traffic signal control with connected and automated vehicles: A survey |
21 |
| What drives the use of ridehailing in California? Ordered probit models of the usage frequency of Uber and Lyft |
21 |
| Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies |
21 |
| A decomposition-based iterative optimization algorithm for traveling salesman problem with drone |
21 |
| A data-driven lane-changing model based on deep learning |
21 |
| Real-time crash prediction in an urban expressway using disaggregated data |
20 |
| Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study |
20 |
| An effective spatial-temporal attention based neural network for traffic flow prediction |
20 |
| Solving the station-based one-way carsharing network planning problem with relocations and non-linear demand |
19 |
| Dynamic ride sharing using traditional taxis and shared autonomous taxis: A case study of NYC |
19 |
| Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles |
19 |
| Short-term prediction of lane-level traffic speeds: A fusion deep learning model |
19 |
