Short-term traffic flow prediction is one of the most important issues in the field of intelligent transportation systems. It plays an important role in traffic information service and traffic guidance. However, complex traffic systems are highly nonlinear and stochastic, making short-term traffic flow prediction a challenging issue. Although long short-term memory (LSTM) has a good performance in traffic flow prediction, the impact of temporal features on prediction has not been exploited by existing studies. In this paper, a temporal information enhancing LSTM (T-LSTM) is proposed to predict traffic flow of a single road section. In view of the similar characteristics of traffic flow at the same time each day, the model can improve prediction accuracy by capturing the intrinsic correlation between traffic flow and temporal information. The experimental results demonstrate that our method can effectively improve the prediction performance and obtain higher accuracy compared with other state-of-the-art methods. Furthermore, we propose a novel missing data processing technique based on T-LSTM. According to the experimental results, this technique can well restore the characteristics of original data and improve the accuracy of traffic flow prediction.

The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the ‘Pareto optimal’ to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.

To date the resilience of transport networks has not been effectively modelled by taking into account the traffic dynamics along with individual drivers’ learning process and irrational behaviours. This study proposes an agent-based day-to-day dynamic model with bounded rationality to capture traffic evolution and drivers’ inertial behaviours when transport networks suffer from local capacity degradation, and variable message signs are incorporated into the proposed model to improve the resilience, which is indicated by the rapidity of recovering to a new approximation equilibrium after disruptions. We employ a small network as a numerical study to conduct resilience analysis, and variable message signs with different compliance rates are utilized to induce traffic flows for alternative routes when a given link of the network is subject to mild (25%), moderate (50%), severe (75%) capacity reduction. The results show that variable message signs can apparently improve the resilience of the network in most of cases, and a larger compliance rate of variable message signs does not necessarily lead to better rapidity of recovery for approximation equilibrium. This study may provide an insight into the resilience analysis and improvement of transport networks under different levels of disruptions, which fully takes into account the individual drivers’ day-to-day learning process, behavioural inertial and the control mechanism of variable message signs with different compliance rates.

The rapid progress in informatisation and electrification in transportation has gradually transferred public transport junctions such as metro stations into the nexus of pedestrian flows, information flows, computation flows and energy flows. These smart environments that are efficient in handling large volume passenger flows in routine circumstances can become even more vulnerable during emergency situations and amplify the losses in lives and property owing to power outage triggered service degradation and destructive crowed behaviours. On the bright side, the increasingly abundant resources contained in smart environments have enlarged the optimisation space of an evacuation process, yet little research has concentrated on the joint optimal resource allocation between transportation infrastructures and pedestrians. Hence, in the paper, we propose a queueing network based resource allocation model to comprehensively optimise various types of resources during emergency evacuations. Experiments are conducted in a simulated metro station environment with realistic settings. The simulation results show that the proposed model can considerably improve the evacuation efficiency as well as the robustness of the emergency response system during emergency situations.

Parcel lockers have continuously growing in popularity as an alternative mode for last-mile delivery services due to their capability of effectively alleviating the risk of a delivery failure, increasing the possibility of delivery consolidation, and reducing the number of drop-off sites. However, poorly located of parcel lockers be less efficient. When determining the parcel locker location, inadequate consideration of uncertain demands can potentially increase the risk of unsatisfied demands. To remedy this issue, a robust optimization model is proposed in this paper with consideration of the demand uncertainties, including the large and small parcels to be received and sent. Not only can the collection locations be optimally determined, but so can the number of large and small parcel lockers for each location at the same time under various robust levels. Meanwhile, the sites whose demands are covered by one of the collection locations are also determined by the constraints of acceptable walking distance. A series of numerical experiments has been performed to evaluate the proposed model, with the main focus being on the solution robustness. Since the set of non-linear constraints are transformed into the linear counterparts, the robust solution can be obtained by the existing solvers within a reasonable time with moderate computing power. The experimental results also provide useful guidance for the practical application of the method, as slightly more conservative decision making can secure the solution robustness with only a marginal increase in costs.