At present, digital maps can estimate the travel time of each trip’s route but cannot offer a fuel consumption estimation at the same time. In this paper, we develop a fuel consumption model based on the Vehicle Specific Power (VSP) distribution, which can connect the traffic condition prediction with the fuel consumption model to predict fuel consumption. First, the traffic condition forecasting and the trip time of each route can be obtained through the digital map Application Programming Interface (API). Secondly, the users need to provide the engine displacement of their vehicles to match the fuel consumption model. Then, the fuel consumption prediction application based on Android is developed to forecast the fuel consumption by using traffic prediction data. Finally, the fuel consumption provided by the On-Board Diagnostic (OBD) data is used to verify the proposed application, and the forecasting error is less than 20%.

One of the key reasons why the road capacity of urban roads in China often fails to meet the designed capacity is the mixture of heavy vehicles (slow-moving) and light vehicles (fast-moving). This paper presents a two-lane cellular automaton model suitable for simulating urban road traffic that includes intersections, based on the NaSch model. The model comprehensively takes into account multiple key factors, such as vehicle safety distance, speed differences between adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time, enabling it to more realistically reproduce the complex characteristics of mixed traffic flows on urban roads. The paper investigates and analyzes the influence of traffic flow density and the proportion of heavy vehicles on the moving bottleneck effect in urban roads from several aspects, including space–time evolution diagrams, traffic flow, average speed, and lane-changing rates. The results indicate that the model established in this paper successfully simulates the characteristics of mixed traffic flows on urban roads, and the simulation results align with actual traffic conditions, achieving the expected simulation effects. Before the traffic volume reaches saturation, the higher the proportion of heavy vehicles on urban roads, the stronger the moving bottleneck effect. This confirms the necessity of conducting research on the phenomenon of moving bottlenecks and the mechanisms of traffic impacts in urban roads, providing a scientific basis for formulating effective traffic dispersion measures and alleviating traffic congestion in the future. This research possesses significant practical meaning and value.

Overflow identification and control at urban road intersection is a derivative problem of oversaturation control. The existing cross-section detector uses the road cross-section occupation of vehicles to identify the overflow state; then, the downstream road condition is often ignored. At present, more and more advanced traffic detection technologies, such as vehicle-to-infrastructure and wide-area radar, can provide reliable data for accurate overflow identification. In this paper, a new method of overflow identification and control at intersections is proposed by using advanced wide-area radar detection data. As a detector for specific segment of road, the wide-area radar can detect the traffic flow data in a certain range of road and provide more data types. Therefore, the average speed and space occupancy of the effective detection road segment are selected as subindicators to establish the comprehensive identification index of overflow identification. Then, the overflow control strategy is developed considering the traffic demand of the overflow phase and the nonoverflow phase. It is proved that the method is more accurate and effective in overflow identification and control by using simulation experiment of field data.

This paper improves the level of urban traffic control by creasing the dimension of control variables. It focuses on roads rather than vehicles. A new space-time resource scheduling model and a bi-level optimization control method for urban intersections are developed in this study. In traditional concept, the properties of lane are fixed. Nowadays, it changes with the development of new technologies, which increase the dimension of the control variables in the control model and expand the control capability. To this end, the space-time resource scheduling model for intersections includes spatial variables (lane genes, phases, and phase sequences) and time variables (green light time of phases). Then, a new bi-level optimization control method is developed, in which there are an upper layer for lane control based on reinforcement learning and a lower layer is a two-layer optimal control method of phase control based on the model predictive control idea. Finally, the proposed method is proved more efficient than traditional methods after comprehensive experiments.

Existing recommendation algorithms treat recommendation problem as rating prediction and the recommendation quality is measured by RMSE or other similar metrics. However, we argued that when it comes to E-commerce product recommendation, recommendation is more than rating prediction by realizing the fact price plays a critical role in recommendation result. In this work, we propose to build E-commerce product recommender systems based on fundamental economic notions. We first proposed an incentive compatible method that can effectively elicit consumer's willingness-to-pay in a typical E-commerce setting and in a further step, we formalize the recommendation problem as maximizing total surplus. We validated the proposed WTP elicitation algorithm through crowd sourcing and the results demonstrated that the proposed approach can achieve higher seller profit by personalizing promotion. We also proposed a total surplus maximization (TSM) based recommendation framework. We specified TSM by three of the most representative settings - e-commerce where the product quantity can be viewed as infinity, P2P lending where the resource is bounded and freelancer marketing where the resource (job) can be assigned to one freelancer. The experimental results of the corresponding datasets shows that TSM exceeds existing approach in terms of total surplus.