Real-time transportation information is the foundation and ensure of dynamic route guidance system. Stationary sensors' detecting precision is high. But because stationary sensors only can detect point information of links, stationary sensors' maturity degree is bad. On account of mobile sensors' detecting links' livelong transportation information, mobile sensors' maturity degree is high. However because of GPS data's errors and probe vehicles' randomness mobile sensors' detecting precision is bad. Considering colligating the mobile and stationary sensors' advantage, this paper proposes a new mobile and stationary sensor fusion model based on BP neural network to improve the accuracy and maturity degree of estimating travel time. The model consists of three modules: (1) mobile detecting module which measure first part, second part and third part travel time over a link using taxis equipped with differential global positioning system receivers; (2) loop detecting module which measure travel time using fixed detectors fixed in roads and traffic signal timing parameters; and (3) data fusion module which uses a neural network to combine outputs from the above two modules to improve the travel time estimation accuracy. This model's inputs respectively are: travel time detected by mobile sensors, travel time detected by stationary sensors, mobile sensors' density in the link and stationary sensors' density in the link. This model's output is the link's travel time. To validate the validity of this model, this paper presents the test of this model using a great deal of real data in Guangzhou city. The result indicates that this model is valid

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%.

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections, considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of "sacrificing efficiency for fairness." Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

In the actual operation of a bus, due to the influences of the passenger flow, traffic conditions and other factors, the vehicle back-station time is often delayed, which brings difficulties in commuting according to a timetable that results in the discontinuity of the bus. This is also the main disadvantage of static bus scheduling. Therefore, the “Entropy model of dynamic bus dispatching based on the prediction of back-station time” is proposed, which can be used for decreasing the passive effect of discontinuity by extending the departure interval of an early bus in advance, and to realize fairness in adjustments of the departure interval by using entropy theory. Finally, the model is validated by two examples, and the results show that the model can match the distribution pattern of the bus departure interval before and after an adjustment and as far as possible, it can reduce bus breaks, balance the occupancy rate and improve the stability of bus operations.

There is more than one link between two nodes with the same direction in public transportation networks described by past description, which induces that shortest path problem in public transportation networks can not be solved. This paper brings forwards a new description of public transportation networks that can resolve the problem. So classic algorithms for shortest path problems can be adopted to public transportation guidance system. Three single-objective and a multi-objective public transportation guidance models based on this description are also proposed to satisfy different passengers. This paper also extends the A* algorithm to shortest route problems in public transportation guidance systems. The developed algorithm is implemented with Guangzhou City public transportation network containing 471 routes and 1040 stops. The results indicate that the description, multi-objective model and adaptation of A* algorithm proposed in this paper are efficient.

The walking time in metro stations is influenced by passenger flow with large fluctuation. Therefore, this paper proposes a method of station walking time calculation considering the influence of passenger flow: firstly, the time, entry, and exit direction and volume distribution characteristics of station passenger flow are analyzed, and the threshold of passenger flow that affects walking time is defined; then, an entry and exit walking time calculation model is established by five independent travel time chains and three station walking time constraints, and the transfer walking time is calculated based on the entry and exit walking time results; finally, the accuracy and validity of the walking time projection model are verified by using the calculation results. The results show that the calculation results are accurate and the validity of the model reaches 91.5%, and the constraint effect on walking time at station time is most obvious when the passenger flow reaches 3000 passengers per 30 min.