Fiber Bragg Grating Sensor Instrumentation for Parking Space Occupancy Management

This work aims at providing an effective parking management system by reducing the drivers' searching time for vacant car-parking space, in turn improving the traffic flow in the car park areas. This is achieved by the use of Fiber Bragg Grating Sensor (FBG) sensor instrumentation in vehicle parking management system. Present work involves embedding an array of FBG sensors underground in the parking space, then determining the strain changes on the FBG sensor due to load applied by the vehicle parked in the parking space, occupancy of the parking space is determined. To validate the FBG sensor parking management system, three most common cases have been considered. This closed loop FBG parking management system can give real-time feed-back to space-guidance display board helping the driver in maneuvering the vehicle to the appropriate parking space. The proposed technique offers optimized usage of parking space for the various segments of cars and also facilitates in a conjoined automated billing system, as compared to conventional method of parking systems.

Automatic Road Extraction using High Resolution Satellite Image Based on Texture Progressive Analysis and Normalized Cut Method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalizedcut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.

Automatic road extraction using high resolution satellite images based on Level Set and Mean Shift methods

Analysis of high resolution satellite images has been an important research topic for urban analysis. One of the important features of urban areas in urban analysis is the automatic road network extraction. Two approaches for road extraction based on Level Set and Mean Shift methods are proposed. From an original image it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. The image is preprocessed to improve the tolerance by reducing the noise (the buildings, parking lots, vegetation regions and other open spaces) and roads are first extracted as elongated regions, nonlinear noise segments are removed using a median filter (based on the fact that road networks constitute large number of small linear structures). Then road extraction is performed using Level Set and Mean Shift method. Finally the accuracy for the road extracted images is evaluated based on quality measures. The 1m resolution IKONOS data has been used for the experiment.

Optimization of bus allocation to depots by minimizing dead kilometers

In metropolitan cities, public transportation service plays a vital role in mobility of people, and it has to introduce new routes more frequently due to the fast development of the city in terms of population growth and city size. Whenever there is introduction of new route or increase in frequency of buses, the nonrevenue kilometers covered by the buses increases as depot and route starting/ending points are at different places. This non-revenue kilometers or dead kilometers depends on the distance between depot and route starting point/ending point. The dead kilometers not only results in revenue loss but also results in an increase in the operating cost because of the extra kilometers covered by buses. Reduction of dead kilometers is necessary for the economic growth of the public transportation system. Therefore, in this study, the attention is focused on minimizing dead kilometers by optimizing allocation of buses to depots depending upon the shortest distance between depot and route starting/ending points. We consider also depot capacity and time period of operation during allocation of buses to ensure parking safety and proper maintenance of buses. Mathematical model is developed considering the aforementioned parameters, which is a mixed integer program, and applied to Bangalore Metropolitan Transport Corporation (BMTC) routes operating presently in order to obtain optimal bus allocation to depots. Database for dead kilometers of depots in BMTC for all the schedules are generated using the Form-4 (trip sheet) of each schedule to analyze depot-wise and division-wise dead kilometers. This study also suggests alternative locations where depots can be located to reduce dead kilometers. Copyright (C) 2015 John Wiley & Sons, Ltd.