Improve Bus Tracking System
Improve Bus Tracking System
The objective of this given memo is to describe the purpose of employing a tracking system in transportation to avoid sector to avoid passengers getting stranded in bus stops, get cold in ice winter-stricken station lobbies without hopes of boarding a bus in the next free minutes. The attached is the methods that prove the effectiveness of an improved bus tracking system – the global positioning system (GPS) service.
The methods for testing the service are discussed as well. How an improved bus tracking system, modern technology enables passengers to track their bus and know their exact locations is described. The tracking system indicates when the bus will arrive at a station and how fast the vehicle is moving. These reporting capabilities of the system have provided an outstanding customer service as well as improving the efficiency, profitability, safety and security in the last decade. Without the system, Inaccurate bus waiting time, buses always delaying for over ten minutes, and inconvenient especially in winter seasons or on weekends is the rhythm of the day.
In this report, Improve Bus Tracking System by some tracking systems – global positioning system (GPS), Radio-frequency identification (RFID), and Stick schedule (Enforcement on punctuality in bus drivers) are discussed as methods of improvement in the passenger service. The report describes how this system have sorted out the problems that existed without the technologies. How the transport system has employed the technology as well as the bottom lines of the project.
If you have any query or concerns about the report, feel free to contact me. Thank you for your time and consideration.
Abstract
Rather than venturing out beyond the curb to notice if a bus is coming, the Columbus transit administration just wants passengers to be able to check their smartphones, tablets, or even their iPhones and trace the next bus, the time it will arrive and the time it will have to departure. The lucky urban community members are the college students; they can spend more time studying in their rooms or sleeping rather than spending time in the chilling cold winter waiting for the bus. Passers-by are as well secure as walking in the city is safer than it used to be. The management of the transit sector has been an easy go: managers can trace their vehicles wherever in the city. Any unauthorized idling in the bus, burglar attack and abusive language in the vehicle can be traced by the concerned authority.
The global positioning system, is the master of modern bus tracking system. GPS originally referred as the NAVSTAR GPS is a satellite controlled radio and navigation system that enables a unlimited number of GPS receivers positioned anywhere on earth to accurately determine the position, velocity and time of arrival of a bus. In the transport system, passengers are impressed by this service as they will never get stranded in the bus station for over ten minutes. They can confirm that there is a bus in the next few seconds. The RFID capability is working excellently in the transport system. In the report, the methods of describing the effective use and performance of this tracking system are explained. Also, tools AgGPS132 GPS receiver, the 5700 GPS receiver and the Digital Orthophoto Quarter-Quadrangle (DOQQ 2000) are employed to explain the working of the tracking system. Segmentation of the highways performed to locate the points of testing.
Figure 1 Study area of interest in Columbus, Ohio 7
Figure 2 Loop detectors on the highways 8
Figure 3GPA probe vehicle positions snapped to the highway centerline. 13
Figure 4Comparison of speed between the GPS data and that of loop detector data. 15
Figure 5The lane frame along a highway with regard to centerline for north run 17
Figure 6Lane frame representation for north run 17
Figure 7Position of the vehicle on the highway for the north run 18
List of tables
Solving the problem of people being late due to the inconsistency of bus timing is the main objective of a real-time bus tracking system. The real-time bus system of follow-up has become more and extremely popular service as devices using the GPS service are becoming more readily available. Smartphones, iPhones, tablets, and Android gadgets are designed to support the application for easy location of vehicles of interest in the highways. For instance, the Wave Transit, the local bus service provider in Wilmington, North Carolina, employs the system where the transit buses send their location coordinates – latitude and longitude and other data – to the central database after every 30 seconds. This service enables a dispatcher or passenger to locate all buses at a given time (Phan, Montanari, & Zerfos, 2010).
The importance of and improved bus tracking system is to enable passengers to access easily the data related to the bus they are interested in. In a GPS system, for instance, display monitors help show the estimated arrival time of the bus to avoid standing for over ten minutes in the bus station. The system automatically calculates the estimated time using mathematics and statistics methods (Parker, 2008). The display serves to provide news and information about the whole Transit system. For example, a message is displayed to broadcast the downtime of a bus and possible emergencies that arise in its ways such as accidents, traffic jams, and abduction by the traffic police as well as a breakdown of the bus (National Research Council, 2003).
The improved bus tracking system makes real-time data available to the public (passengers) as well as the owner company. According to International Energy Agency (2002). The service’s website displays the list of all available buses and passengers can click to view the real-time map for each bus. On the navigation window of the system, the route, the next stop, estimated arrival time, and the direction of the bus are all shown. Why then does a passenger has to wait for hours waiting with all this information? Moreover, the pages are designed in such a way that the passenger can pull up the maps on their mobile device while standing at the bus station and view the bus make its way to the station. Since a good number of users use smartphones, the website detects which mobile browser needs the information. It formats this information to a version of a smaller screen. The data presented on the screen in the station is invaluable to the interested passengers (Nakanishi, and Fleming, 2011). Passengers can quickly and easily realise the time their bus will be at the specific station. These information helps to improve the ridership within the transport area. Perception of this bus service has improved because their technology is excellent. Major cities in the United States, Columbus, in particular, have benefited from this technology.
1.2. Background and related work of improved bus tracking system
Currently, the United States’ are employing bus tracking applications in place. Particularly, Columbus metropolitan transport authority has released a pilot project. Thy has constructed a web page that displays routes within the city with information on each station showing the distance the bus is away from the stop. In the web page, a bus icon is shown to indicate where each bus is on the map. The web page is a superb mobile friendly service as well – it allows users to access it at their convenience. This Metropolitan Transportation Authority updates the map with a newer data after every thirty seconds. In Columbus, transport companies provide applications for this service. These companies offer a real-time map at the station, on the web and anywhere through the mobile application. The map indicates the relevant routes the buses follow, stop, active route colour keys, and the location. Routes are colour coded and have markers that indicate where buses stop and the estimated arrival times at stations (In Ukkusuri, & In Özbay, 2013).
Columbus uses an application provided by the Next Bus Incorporated and the application is not map-centered, rather, it incorporates the use of a form with drop downs that allows the passengers to select the route, direction and the station/stop. An option for Google viewing is provided on the final page. On the Google map, the route, direction, and the next departure time are drawn. This allowance enables the application to hide or indicate vehicles and stations on the map by the use of a checkbox. A unique feature of this service is its ability to choose several routes shown on the map. There’s less much of it as a mobile user using the application can locate the bus image on the map noted with blue lines.
2. Methods
With the rapid growth of vehicle volume on roads, the urban road traffic system performance is a critical concern to the key planners of transportation, pedestrians and members of the urban community. Evaluation of performance measures depends on the effectiveness and reliability of the collected traffic data. In this report, methods of extracting transport using GPS receivers integrated with GIS information. In the method, an AgGPS132DGPS receiver was used to probe vehicle data along some highways in Columbus, Ohio. Also, a digital orthophoto quarter-quadrangle (DOQQ 2000) of ground resolution 0.15 metres was used to develop the database of the highway.
2.1 Data Collection
The regions of interest in this study included a number of freeways in Columbus including the southern part of SR 315, and the I-70/I-71 overlap where two interstate highways merge in downtown – typically bound by I-670 north, I-70/I-71 south, SR 315 west, and I-71 east), and I-71 north of I-70 shown in figure 1.
Figure 1 Study area of interest in Columbus, Ohio
Figure 2 Loop detectors on the highways
For the freeways, particularly on the region I-70/I-71, massive problems were encounter – congestions, traffic delays, as well as safety hazards (accidents and geometric failures). In a day, an estimate of hundred and seventy-five thousand vehicles pass through I-70/I-71. This section is referred to as the most congested region of the highway in the whole state. Besides the congestion, an average of three accidents is registered every day.
The GPS probe vehicle information was gathered. An AGGPS132 receiver with a sub-meter differential effectiveness was employed to collect the data. The collection time of data included the AM peak (between 7 am and 9 am) and the peak PM ( between 4 pm and 6 pm) periods in three consecutive days – Tuesday, Wednesday, and Thursday – that were considered the typical weekdays for traffic analysis. The route that probe followed for each trip was fixed: the driver drove onramp to SR 15 SB from Lane Avenue, crossed the SR 315 lanes to I-70 EB/I-71 NB and drove to I-71 NB. He took the offramp from the Polaris Exit, drove to I-71 SB, I-70 WB/I-71 SB and SR 315 NB back to Lane Avenue exit. Typically, for every peak period for the data collecting day, the driver completed two runs.
The Trimble 5700 RTK receiver was used to test the performance of eth AgGPS132 DGPS receiver on the runs and to do the highway linear referencing. During the test, the AgDGPS132 receivers and the Trimble RTK were placed on the same car with the antennae separated by 0.6 metres. Since the GPS receiver record location as latitude-longitude pairs, Trimble 5700 receiver was used to gather the mile marker for highway linear reference system. The post-processing technique with a Trimble Geomatics Office programme and base station data was used to find the position data of GPS from the 5700 devices.
The Digital Orthophoto Quarter Quadrangle (DOQQ) was employed as the backdrop to overlay the transportation of GPS data as well as the development of spatial information. The DOQQ machine offered a high resolution of about 0.15 meters that referred to as the distance on the ground represented by the pixel in the x-y directions. All the DOQQs were referenced in the North American Datum of 1983(the NAD 83). Loop detector data were obtained for the highways of interest as shown in figure 2 to reference the GPS probe vehicle data. The points indicated on figure 2 show the location of loop detectors. The loop detector data contained the velocity for each lane and the estimated mean velocity data over the lanes in five minutes and thirty seconds (5.5 minutes) intervals.
3 Data processing
3.1 Highway frame
A digitalized highway network was used to analyze the GPS probe vehicle data. Existing data files was the main approach used here – Topological Integrated Geographic Encoding and Referencing (TIGER) developed by the Census Bureau of the United States. The TIGER map provided a representation of the transportation geometry. The GPS receiver was used to generate the road network of the interested highways. The base map was constructed directly from the GPS data. For this study, many areas of interest was the downtown Columbus where tall buildings and overpass bridges caused problems with multipath problems and data missing in the gathered GPS data.
In the study, the method of digitizing the centerline “heads-up’ was used on a computer sing the DOQQ as backgrounds of the image. A distance of twelve feet (approximately 3.7 meters) between adjacent lanes and a spatial resolution of 0.15 meters were adequate for verifying the centerline. A node was placed at all the road changes and the off-ramps. The two centerline shape files were converted to a digital format; one for the north run and the other for a south run in the GPS probe information. In the north run, the segments of the SR 315SB, the I-70EB/1-71EB, and I-71 NB. On the other hand, the south run contained the routes of I-71 SB, I-70 WB/ I-71 WB, and the SR 315 NB as shown in the figure 1 above.
3.2 Linear reference system procedure
ODOT – Ohio Department of Transportation – posted the mile marker system for linear referencing of the digitized highway network. To obtain the mile marker points information, the ODOT individuals also checked the highways of interest. The mile markers started at zero at the western boundary of each county line. The mile markers were located with intervals of one mile starting from zero at the western state line and continued across the whole state. Since it was difficult to obtain the datum “mile zero” of the interstate highway, the mile marker posted along the highway was used as the measured anchor point in the linear referencing. The Trimble 5700 GPS machine was used to measure locations of five-marker points to establish linear referencing system. The distance segment along the highway was designed by the use of a computer programme and the computed mile marker assigned for each node of digitized network.
3.3 Map GPS probe vehicle positions to a digitized centerline
The collected spatial GPS data required to be integrated into the linear referencing system after linear referencing of the highway was established. A snap program was developed to snap the position of the vehicle to the nearest point of the centerline. This program was edited in visual basic 6.0 by the use of MapObjects. In the program: the GPS data ShapeFiles were converted from a raw data gathered from the receiver, and highway centerlines were digitized. In the program, the following logistics were followed: the highway centerline ShapeFile was loaded and a GPS shape file loaded. A new GPS shape file that could store the snapped point on the centerline was created. Initiating with the first position in the GPS ShapeFile, a move through each point was started:
For every point, the perpendicular distance between the GPS point and the highway centerline section was calculated.
The minimum absolute value of the Point_Distance, the corresponding Closest_Segment, as well as the corresponding point on the centerline, were determined.
The longitude and latitude for the Closest_Point distance were calculated.
The new GPS shapefile data were recorded: Centerline ID, Closest_Point feature, the Point_Distance overall length of Closest_Segment, distance from the initial node of the Closest_Segment to current GPS position, linear reference evaluated basing on the distance obtained in the final step.
Figure 3 shows how the GPS positions were snapped to the highway sections using the four lanes. The distance between the position of the vehicle and the centerline was about 10 feet and was less than 12 feet that indicated that the vehicle was on eth second lane. The point 2 is on ethe lane number 4 since the distance from the GPS position to the centerline lies within the interval of twenty-four feet to twelve feet. Point 3 was discarded when snapping was taking place as it was located far away from the centerline. Table 1 shows the above GPS data.
Figure 3GPA probe vehicle positions snapped to the highway centerline.
3.4 segment highways to half-mile length
The GPS probe vehicle information recorded the essential data for each position along the highway at every second of the travel. These pints were aggregated to determine the traffic flow pattern. Two ways to aggregate the GPS points were used; one, aggregate all the positions along the particular segment of the road; two, aggregate all positions for a fixed period. In the study, the traffic pattern along the road section was the primary objective. The first method was used to aggregate the GPS points. The highway was sectioned to a specific length either in a fixed-length or variable length. The fixed-length segment controlled the location and measured the attributes of the interest for these segments. This technique was employed to subdivide the entire network into half-mile segments.
3.5 comparison of GPS probe vehicle velocity with loop detector velocity.
A comparison between the speed plotted from the GPS receiver and the speed collected by the loop detector was made. The thirty-second-speed data were employed for comparison with the vehicle probe data. The two nearest GPS probe vehicle positions to the loop detector station for every day when the GPS information was gathered were determined. Also, the time and the location of the lane for the two GPS points were confirmed and used to extract the speed data from the loop detector dataset. For the location of the lane, a period of about thirty seconds was allocated containing the specific GPS probe vehicle location. The speeds from the two GPS locations were averaged and the mean speed compared with the speed derived from the loop detector. This can be illustrated as shown in figure 4 below.
Figure 4Comparison of speed between the GPS data and that of loop detector data.
4. Results
4.1Analytical error of the GPS system
The 5700 machine had a sub-centimeter accuracy and was generally employed for survey purposes. The Trimble machine was utilized to examine the accuracy and reliability of the AgGPS132 GPS receiver to obtain the positions of the probe vehicle on the fixed run. Data collected from by the AgGPS132 receiver and that by the Trimble receiver were overlaid at the same time. The distance between the point pairs was calculated. Table 3 below indicates the statistics information for locational difference of the two GPS receivers after the abnormal GPS points were removed. In te results, the negative signs show that the 5700 machine was behind the AgGPS132 device; the positive sign/values show that the 5700 machine was ahead of the AgGPS132.
With a sub-meter accuracy of the AgGPS132 device, it was possible to identify the location of the lane of a vehicle on the highway. Also, with the lane information on the network, a detailed frame was modelled in terms of lanes. The figure 5 illustrates the frame around the centerline digitized for the north run. The frame indicates the position of the lanes and possible bridges on the highway. In this illustration, the frame was useful as it identified the exact lane the vehicle was located when the GPS probe vehicle data was overlaid. In figure 6, the configuration of the lane frame for the run. The configuration is much practical as the frame indicates the initial position of the lane, where it continues and where it ends. It was easier to determine whether the vehicle proceeded on the same lane or change the lane. Figure 7 shows the vehicle run.
Figure 5The lane frame along a highway with regard to centerline for north run
Figure 6Lane frame representation for north run
Figure 7Position of the vehicle on the highway for the north run
4.3 congestion analysis
Analysis of congestion on highways is a critical exercise of evaluating the performance of roads. To estimate the congestion levels, travel-time-based measures were put in place.travel time and speed measures are flexible and essential for a broad range of analyses. The speed data can be found from time and positions provided by the GPS receiver, time of travel, mean speed of travel, and the index of congestion were based on the travel time were chosen for measuring the congestion for the Columbus study region. The average times of travel for the entire north and south runs were evaluated and represented as shown in table 4 and table 5 below. The starting point for the calculation of time is where the Lane Avenue met the SR 315; the ending point was positioned at the Polaris exit.
1 mph = 1.61 km/hr.
This paper discusses the art of bus tracking system as global positioning system is the key aid to this art. The GPS bus tracking system as discussed in the paper has improved the transportation of passengers, safety of the urban community as well security of shuttle owners. The GPS system uses a satellite technology and sophisticated computer designing to track the buses on the roads. According to the paper, the estimated time of arrival of the buses can be supposed with extreme accuracy as the estimates are updated in real time (Wiegand, 2010). The improved service improves the operation of the buses. The system also assists the operation managers by having a user-friendly tool to enable in managing, monitoring and reporting the fleet information of the vehicles.
The GPS tracker enables the establishment of a “big picture” view of the buses. Most importantly to the passengers, the technology helps to reveal the schedule consistent time of arrival and inefficient processes (Weiner, 2013). The technology helps the managers to identify the problems that exist in the transportation and implement improvements. Passengers benefit from the advanced GPS bus tracking system in several ways. With this fleet tracking system, there is no more shivering in the cold in winter. The GPS contains the real-time alerts as well. According to the paper, bus riders waste less time enabling them to plan their schedule. To the college students and large campus learners, they will spend more time studying or sleeping instead of waiting for the delaying bus (Hartman, Kurtz, and Moser, 1994).
According to the report, the bus tracking enables the passengers to know where the vehicles they are waiting are all times. The passenger knows if the vehicle they are waiting failed to follow its schedule route (Xiong, 2012). The system tells the passenger if the bus fails to leave or go back where it is scheduled to. The fleet tracking service enables to monitor any unauthorized use of the vehicle, unauthorized speeding and cases of unnecessary idling in the bus. All these services impact the profitability of company.
6. Conclusion
In the paper, an integrated GPS-GIS methodology for traffic information extraction of data is described (Maccoby, 2013). Spatial characteristics of the highways were developed using a GIS that was based on the DOQQ of superb resolution. The linear referencing used by professional to indicate the location as the distance from known initial point in a specific direction. Both the AgGPS132 receiver and the 5700 receivers worked well to realize the benefit this technology has caused to the transport sector.
References
Hartman, R. J., Kurtz, E. Moser, E. K., (1994). Transit Cooperative Research Program. National Research Council (U.S.)., United States., & Transit Development Corporation. Incentive programs to improve transit employee performance. Washington, D.C: National Academy Press.
In Ukkusuri, S. V. S. K., & In Özbay, K. (2013). Advances in dynamic network modelling in complex transportation systems.
International Energy Agency. (2002). Bus systems for the future: Achieving sustainable transport worldwide. Paris, France: International Energy Agency.
Maccoby, M. (2013). Transforming health care leadership: Systems guide to improving patient care, decrease costs, and improve population health. San Francisco, CA: Jossey-Bass, A Wiley Brand.
Nakanishi, Y. J., Fleming, W. C., (2011). National Research Council (U.S.)., Transit Cooperative Research Program., United States., & Transit Development Corporation. Practices to protect bus operators from passenger assault. Washington, D.C: Transportation Research Board.
National Research Council (2003). Transit Cooperative Research Program., & États-Unis. Real-time bus arrival information systems. Washington, D.C: Transportation Research Board.
Parker, D. J. (2008), National Research Council (U.S.)., Transit Cooperative Research Program., United States., & Transit Development Corporation. AVL systems for bus transit: Update. Washington, D.C: Transportation Research Board.
Phan, T., Montanari, R., & Zerfos, P. (2010). Mobile Computing, Applications, and Services: The First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Berlin, Heidelberg.
Wiegand, D. M., (2010). National Research Council (U.S.)., Commercial Truck and Bus Safety Synthesis Program (U.S.), & United States. Special safety concerns of school bus industry. The Washington, D.C: Transportation Research Board.
Weiner, E. (2013). Urban transportation planning in United States: History, policy, and practice. New York: Springer.
Xiong, G. (2012). Service science, management, and engineering: The theory and applications. Amsterdam: Elsevier/Academic Press.
