Analysis of interrelationship between pedestrian flow parameters using artificial neural network

Pedestrian flow parameters are analysed in this study considering linear and non-linear relationships between stream flow parameters using conventional and soft computing approach. Speed-density relationship serves as a fundamental relationship, Single-regime con- cepts and deterministic models like Greenshield and Underwood were applied in the study to describe bidirec- tional flow characteristics on sidewalks and carriageways around transport terminals in India. Artificial Neural Net- work （ANN） approach is also used for traffic flow mod- elling to build a relationship between different pedestrian flow parameters. A non-linear model based on ANN is suggested and compared with the other deterministic models. Out of the aforesaid models, ANN model demonstrated good results based on accuracy measure- ment. Also these ANN models have an advantage in terms of their self-processing and intelligent behaviour. Flow parameters are estimated by ANN model using MFD （Macroscopic Fundamental Diagram）. Estimated mean absolute error （MAE） and root mean square error （RMSE）values for the best fitted ANN model are 3.83 and 4.73 m/ min, respectively, less than those for the other models for sidewalk movement. Further estimated MAE and RMSE values of ANN model for carriageway movement are 4.02 and 4.98 m/min, respectively, which are comparatively less than those of the other models. ANN model gives better performance in fitness of model and future prediction of flow parameters. Also when using linear regression model between observed and estimated values for speed and flow parameters, performance of ANN model gives better fitness to predict data as compared to deterministic model. R value for speed data prediction is 0.756 and for flow data pre- diction is 0.997 using ANN model at sidewalk movement around transport terminal.

A data mining approach to characterize road accident locations

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. Theassociation rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

Traffic Emission Control： A Knowledge Based Approach

At present the entire world is under the risk of severe environmental problems, due to the expansion of industries, urban population and commercial activities, the city like Delhi （India） faces transportation, environmental and economic challenges. Such type of situations demand the addition of knowledge based layer to help the operators to be familiar with exact traffic problem and give the best choice of strategic control actions to the city. In current situation there is a necessity to build systematic, knowledge based tool to analyze and manage the recent or potential air quality issues and traffic noise issues. The paper comprises the creation of knowledge from the information which is extracted from the various data by using knowledge based modules （spreadsheets, database, software, etc.） and some management, optimization models. Such type of knowledge based management tool may act as a Decision Support System （DSS） which will be very supportive in traffic control system. The technology of knowledge-based systems may facilitate in designing and executing suitable knowledge structures to formulate conceptual models for traffic analysis and management and to use such approach for on-line strategic traffic management operations.