No abstract

Intersections are the bottlenecks of the road network. The capacity of signalized intersections restricts the operation of the road network. Dynamic estimation of capacity is necessary for signalized intersections refined management. With the development of technology, more and more detectors were installed near the intersection. It had been the information-rich environment, which provided support for dynamic estimation of capacity. A dynamic estimation method for a saturation flow rate based on a neural network was developed. It would grasp the dynamic change of saturation flow rates and influencing factors. The measure data at three scenarios (through lanes, shared right-turn and through lanes, shared left-turn and through lanes) of signalized intersections in Beijing were taken as examples to validate the proposed method. Firstly, the traffic flow characteristics of the three scenarios and factors affecting the saturation flow rate were analyzed. Secondly, neural network models of the three scenarios were established. Then the hyperparameters of neural network models were determined. After training, the neural network structure and parameters were saved. Lastly, the test set data was validated by the training model. At the same time, the proposed method was compared with the Highway Capacity Manual (HCM) method and the statistical regression method. The results show that both regression models and neural network models have better accuracy than HCM models. In a simple scenario, the neural network models are not much different from the regression models. With the increase of complexity of scenarios, the advantages of neural network models are highlighted. In through-left lane and through-right lane scenarios, the estimated saturation flow rates used by the proposed method were 7.02%, 4.70%, respectively. In the complexity of traffic scenarios, the proposed method can estimate the saturation flow rate accurately and timely. The results could be used for signal control schemes optimizing and operation managing at signalized intersections subtly.

Accurate traffic speed forecasting not only can help traffic management departments make better judgments and improve the efficacy of road monitoring but also can help drivers plan their driving routes and arrive safely and smoothly at their destination. This paper focuses on the lack of traffic speed data and proposes a method for traffic speed forecasting based on the multitemporal traffic flow volume of the previous and later moment states. First, according to traffic flow volume data, the different traffic patterns of previous and later moment states were extracted. Second, the performance of five forecasting models, namely, long short-term memory (LSTM), backpropagation (BP), classification and regression trees, k-nearest neighbor, and support vector regression, were compared. Finally, the model with the best prediction results was used to conduct sensitivity analysis experiments for different traffic patterns. Through a real-data case study, we found that the LSTM model has the highest prediction accuracy compared to other models in both time and space. This traffic pattern "previous = 3 and later = 3" can forecast traffic speed more accurately, and its forecasting ability is robust across a range of scenarios.

Service is now considered as the basis of exchange and defined as "the use and application of competencies and knowledge for mutual benefits". In the current stream of development, service systems emerge as widely accepted, interesting and logical area to study the concepts related to service interactions. Service systems are dynamic entities which interact with each other and integrate their resources to co-create value. This paper characterizes Akhuwat Foundation Microfinance project as a Service System with the application of service system framework developed by Lyon and Tracy. The purpose of the study is to evaluate Akhuwat microfinance project to understand its service activities and to define business exchanges from the perspective of reciprocal benefits. Assuming the interprevitist assumptions, a qualitative approach was adopted and data was gathered through in-depth interviews with the employees of Akhuwat microfinance project. Data analysis revealed that Akhuwat microfinance is in fact, a true representation of a service system. Their objectives, processes, control and performances are service oriented. The main focus of transactions with donors, borrowers and other network partners is poverty alleviation, financial empowerment and a sustainable society. This paper motivates future researchers to apply the service system framework in other sectors, and characterize organizations that are, in its true essence service oriented.