GB2421830A - A method and apparatus for processing traffic related data - Google Patents

A method and apparatus for processing traffic related data Download PDF

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GB2421830A
GB2421830A GB0428455A GB0428455A GB2421830A GB 2421830 A GB2421830 A GB 2421830A GB 0428455 A GB0428455 A GB 0428455A GB 0428455 A GB0428455 A GB 0428455A GB 2421830 A GB2421830 A GB 2421830A
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data
route
time
events
transportation vehicle
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Hon Keung Jonathan Wong
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled

Abstract

A method of providing traffic related data comprises the steps of obtaining data for events occurring at a number of locations (P1-P5), processing said data and providing the processed data to at least one end user. The event data may include the time of the event such as the departure or arrival of a vehicle at a specific location. An identity of a vehicle may also be included. The step of obtaining the data may include receiving signals from a vehicle or automatic vehicle locator by a data processing device. The processing device may be a computer which stores the received data. The processing step may include grouping the data according to a time period and the identity of the vehicle. The processing may derive a transit value for vehicles between two points over a predetermined time period. This value may be used to predict future events in the traffic network.

Description

A Method of and an Apuaratus for Obtaining, Processing and Providing
Traffic-Related Data This invention relates to a method of and an apparatus for obtaining, processing and providing data relating to traffic on land, in the sea or the air. While such a method and apparatus are most aptly used in relation to transportation on road in the urban area, it is possible to use such a method and apparatus in other situations. While the present invention will hereinbelow be described with reference to transportation on road, such is not intended in any way to limit the application of the method and the apparatus in other traffic situations. In particular, the term "transportation vehicle" used herein is intended to cover all apparatus for locomotion by land, air or water.
The traffic situation on the road at any given moment is affected by a large number of factors, e.g. whether it is a weekday or a holiday, whether it is during rush hours or lunch time, whether it is raining or sunny, and etc. All such factors have a certain bearing on the traffic situation, It is an object of the present invention to provide a method and an apparatus whereby data indicating the general traffic situation may be obtained, stored, processed, and/or provided to an end user.
It is a further object of the present invention to provide a method and an apparatus whereby a predictive time of occurrence of a future trafficrelated event may be provided to an end user.
According to a first aspect of the present invention, there is provided a method of providing traffic-related data including the steps of (a) obtaining data of events occurring at a plurality of locations along at least one traffic route; (b) processing said data; and (e) providing the processed data to at least one end user.
According to a second aspect of the present invention, there is provided an apparatus for providing traffic-related data, including means for obtaining data of events occurring at a plurality of locations along at least one traffic route, means for processing said data, and means for providing the processed data to at least one end user.
An embodiment of the present invention will now be described by way of an example, and with reference to the accompanying drawings, in which:- Fig. I is a schematic view of a bus route relating to whose situation the method according to the present invention is applied; Table 1 shows hypothetical raw data relating to the bus route shown in Fig. 1, as captured on 3S May 1999 and semi-processed information obtained therefrom; Table 2 shows hypothetical raw data relating to the bus route shown in Fig. 1, as captured 4th May 1999 and semi-processed infonnati on obtained therefrom; Table 3 shows hypothetical raw data relating to the bus route shown in Fig. 1, as captured on 6 May 1999 and semi-processed information obtained therefrom; Table 4 shows hypothetical raw data relating to the bus route shown in Fig. 1, as captured th May 1999 and semi-processed information obtained therefrom; Table 5 shows hypothetical raw data relating to the bus route shown in Fig. 1, as captured on 10th May 1999 up to 1 4:3423 and semi-processed information obtained therefrom; Table 6 shows norm values and coefficient indexes (Cl) calculated from the data in Tables 1 to 5; Table 7 shows hypothetical data for a first application of the method according to the present invention as applied to the bus route represented in Fig. 1; and Table 8 shows hypothetical data for a second application of the method according to the present invention as applied to the bus route represented in Fig. 1.
The present invention may be implemented by information technology devices, such as various electronic devices, optical devices or applicable sensors, computer hardware, computer software packages, e.g. a relational database management system (RDBMS) traded by Oracle Corporation under the name "Oracle TM Object-Relational Data Server", Data Warehouse and Data Mart (see e.g. "DKMS Briefs" by Joseph M. Firestone in http://www.dkms.comJvDMEDhtJ) networking products, communications systems (wired or wireless) and various kinds of switching systems. From the operational and ftinctior,al aspects, a system for implementing a method according to the present invention includes:- a. data acquisition mechanisms, which make use of various kinds of channels and methods.
In particular, such channels and methods include the use of automatic vehicle locator (AVL) technologies, e.g. Global Positioning System (UPS), manual input processes, e.g. manual input via keyboaid, and importation of information or data from other database(s); b. data repository for storing data and/or information obtained after the data are processed; and a. data provision mechanisms for export of data. to other database(s), and knowledge transfer to requestor(s) via various kinds of display device or broadcasting technology, e.g. by radio frequency broadcasting or via a mobile phone system.
A basic characteristic of this invention is the construction of route skeletons/route data structure in the cyberspace/repository, and the mapping of these to traffic routes on the streets, roads, water ways, air ways, arterial or trunk road in the physical traffic infrastructure. For implementing this invention, the following equipment and computer software packages may be used:- i. equipment installed along specific locations along the traffic route as data and/or image capturing devices. Such include devices using digital enhanced communication telephone (DECT) technology, PHS technology, Bluetooth, CCTV camera, camera with MPEO-4 capability, or radar receiver. In less densely populated areas, global positioning system COPS) or defence global positioning system (flOPS), or Location-Capable mobile phone may be considered for avoiding some of the financial and/or technological weaknesses or disadvantages which may arise from the use of roadside short range transceivers; ii. networking services provided by information technology (IT) infrastructure providers, such as public switching telephone networking (PSTN) providers, Internet service providers, or lnternet2 service providers; and iii. relational database management system (RDBMS) packages, data warehouse packages, data mining packages and data mart packages as computer software tools.
By using the above equipment and etc., raw data relating to traffic conditions or mobile objects, e.g. transportation vehicles, may be captured, transmitted, recorded, analyzed, processed, extracted and transformed to become cyber route skeleton data structure attached or linked to relevant data, information, index figures or knowledge relating to traffic status, e.g. in a city.
The cyber route skeleton is built up by applying a normalization process on raw datum packets selected of a same category according to pre-defined criteria. Relative coefficient indices or figures are then obtained on the basis of such results of normalization for reflecting the general traffic condition of the physicaFtrafflcrotrte(s) or"segrrrer4s)*withth*a spetific" traffic route.
By using the route side/roadside data capturing equipment as stationary base station with known location identification, discrete data or image of nearby mobile objects, e.g. transportation vehicles, may be captured and stored in a computer server repository via network service provided by PSTN providers, SonetJSO}l or internetflnternet2 networking within the city information technology infrastructure. The datum packet is in the following format:- (T-Log, L-ID-BS, D-MT1, D-MT2, ...D-MT) in which:T-Log is the time of logging and storing of the datum packet, and represents the time of occurrence of a traffic-related event This may be the system time on the sewer side in a networking service with Quality of Service (QoS) or the system time on the base station of route side in a networking service without QoS.
L-ID-B5 is the location identification of the base station on route side D-MT1 to D-MT are data items relating to the mobile object (e.g. transportation vehicle) which occasions the data capturing, Such data items may consist of two categories:- a. discrete data mode with active capturing method - the data items are in the form of digital format originating from the mobile object, and may include:- i. mobile object identification; ii. mobile object driver identification; iii. number of passengers on the mobile object; iv. occupancy rate of the mobile object.
b. image mode with passive capturing method - discrete data may be extracted by pattern recognition technique from image captured with cameras or sensors in different spectrum. While the conversion of analogue signals to digital signals in compressed digital form is best carried out by the route side equipment, the recognition process is best carried out at sever platform. The quality and quantity of data extracted from frames of image depend on the sophistication and capability of the pattern recognition technique employed and the sensitivity or resolution of the cameras or sensors. Such data may consist ot- i. mobile object count per frame; ii. mobile object count per line of lane; iii. mobile object type or category (e.g. buses, private cars, taxis, motorcycles, lorries etc.); iv. relative distance between mobile objects in the same line of lane; v. mobile object identification.
The raw datum packets are grouped in the order of Date/Time/Base-Station and stored in the cyberspace with RDBMS package in the system server platform.
Seven route types are identified according to their different characteristics:- a. PB-TS Route Type b. FB-DSRoute1jpe c. TR-TS Route Type d. WR-TS Route Type e. AC-DS Route Type f. IMS Route Type g. LO Route Type PB-TS Route Tvp A route of the PB-TS Route Type is built from data derived from public busing and routing in a pre-defined pickup and/or delivery points (P-PD) reserved for bus vehicles in a sequence of which buses traverse in order, starting and ending at a depot. The datum packets naturally form a time component with T-Log as the key, The basic format of raw datum packet is:- (T-Log, L-ID-BS, D-MT) where T-Log is the Arrival Time (T-Arr) or Departure Time (T-Dep) (as the case may be) of a vehicle; L-ID-BS is the location indication information of the base station; D-MT is the targeted mobile data which, in this case, is the Vehicle Identification (IIYV).
The Point of Pickup and/or Delivery (P-PD) may be arranged so as to be close to or to coincide with the location of the route side base station. The Vehicle Identification is the D-MT mobile targeted data. In other words, the nearby mobile object data can be organized in the datum packet with L-ID-B5 Identification and Vehicle Identification as keys. The traversing time of vehicles in the pre-coniigured routes may then be used for constructing a cyber route skeleton.
In this connection, a number of abbreviations and their definition are provided as follows;- T-Arr; "Time of Arrival" for a transportation vehicle relative to a P-PD is the same as the Time of Logging in the datum packet under consideration T-Dep: "Time of Departure" for a transportation vehicle relative to a P-PD is the same as the Time of Logging in the datum packet under consideration TD-fl; This is the length of time during which a transportation vehicle stays at a P-PD, and equals to:- T-Dept - T-Arr of the same P-PD TD-TS: This is the time duration foratransportation vehicle to traverse from a P-PD (P-PD) to a next succeeding P-PD (P-PD4.1) along the same route, and equals to:- T-Arr (of P-P D.1) - T-Arr (of P-PD) TD-TL: This is the time duration for a transportation vehicle to travel from a P-PD (P-PD) to a next succeeding P-PD (P-PD-,.1), and equals to:TD-TS - TD-fl ON-TS: Occurrence Norm of Traversing Time is a norm value calculated with TD-TS of the relevant events occurring in the relevant route or segment of the route, and in a dimension between the point of occurrence and historical point and/or recent samples selected with specific criteria.
HN-TS: 1-listorical Norm of Traversing Tune is a norm value calculated with TD-TS of the relevant route or segment of route in a tune domain of historical points of thne and/or datum packets selected with specific criteria.
CI-TS: Coefficient Index of Traffic Status is an index reflecting the traffic status of a route or a segment of a route. If such relates to a point of occurrence, it is called "Occurrence CI-TS". Otherwise, it is called "Historical CI-TS" or simply "CI-TS". Different criteria for selecting the datum samples, different methods of normalization and different raw data selected will bring about different CI-TS.
We should also take note of the following concepts:- - Normalization such is defined as the process of calculating or defining the norm value of samples among members of a domain - Coefficient Index - such is defined as the ratio between the norm of a domain over the norm of its super domain. Such are used for reflecting the general traffic condition - Time Dimension: Data items or events are viewed or arranged in time sequence Tune Domain: Domain of events arranged in dimension As an example, Fig. 1 is a schematic view of a bus route with P1 as the starting point, PS as the end point, and P2 to P4 as intermediate Points of Pickup and/or Delivery (P-PDs). This route thus consists of four segments, namely P1-2, P2-3, P3-4 and P4-S. Assuming that three buses (Vl, V2 and V3) provide bus service along this route. Their hypothetic Logging Tune in hh:mm:ss format (hour 00-23: minute 00-59: second 00-59) for the days 3 May 1999, 4 May 1999, 6 May 1999, 7 May 1999 and 10 May 1999 are shown in Tables ito 5 respectively, For the purpose of reducing complexity, only the above five days are selected for building the cyber mute with the criteria as the traffic condition between 12:00 noon to around 18:00:00 with non-holiday and regular weather condition. The last day, i.e. 10 May 1999, is the day of occurrence and the current time is assumed to be 14:34:23. It should be noted that as P1 is the starting point of the route where bus drivers have their break, the time of departure and arrival for each bus is handled differently from other P-PDs in respect of traversing calculation.
Take Table 1 as an example, on 3 May 1999, bus Vi departed from P1 at 12:00:35 and traversed for 286 seconds (i.e. 4 minutes 46 seconds) to arrive at P2 at 12:05:21, traversed for 611 seconds (i.e. lOntutes 11 seconds)to arrive atP3 at 12:15:32, traversedfor 181 seconds (i.e. 3 minutes 1 second) to arrive at P4 at 12:18:33, traversed for 603 seconds (i.e. 10 minutes 3 seconds) to arrive at P5 at 12:28:36. The time 12:06:33, 12:17:11, and 12:20:25 denote the actual time at which VI departed from P2, P3, and P4 respectively. Upon arrival of P5, Vi traversed for 1664 seconds (i.e. 27 minutes 44 seconds) to arrive at Fl at 12:56:20, As to the route P5-I, such may or may not pass through P2, P3 and/or P4.
For the same VI, it departed from P1 again at 13:02:55, traversed for 279 seconds to arrive at P2 at 13:07:34, traversed for 595 seconds to arrive at P3 at 13:17:29, traversed for 266 seconds to arrive at P4 at 13:21:55, traversed for 502 seconds to arrive at PS at 13:30:17, and traversedfor 1806 seconds to arrFve at P1 at 14:00:23. The time 13:10:52, 13:18:38, 13:23:21 and 13:3 1:37 denote the actual time at which VI departed from P2, P3, P4 and PS respectively.
For VI, the maximum TS value for the route segment P1-2 (TS-P1-2) on 3 May 1999 is 286 seconds (denoted by a subscript a), and the minimum TS value for the route segment P1-2 on 3 May 1999 is 154 seconds (denoted by a subscript m) . The norm value (denoted by a subscript n) is calculated by first removing the maximum TS value and the minimum TS value.
An arithmetic mean is then taken of the remaining four TS-Pl-2 values, i. e. (279+247+268 259)/4 263 For the Occurrence Norm in this example, such is defined as in the following Formula 1:- Norm of Occurrence = E,1-1,, (TSx fl)/tncljn where TS is the Traversing re of Logging Events of all relevant vehicles within the timeframe window from point of occurrence and backward to historical point of time per relevant segment or route. The Logging Even; indexed as I, is the oldest and Event j is the most recent one.
In general, sample selection criteria and norm calculation definition are subject to specification or requirements from system designer(s) and/or end user(s). For instance, the selection criteria, instead of referencing to tiineframe window, can use number count of historical Logging Events from Point of Occurrence, In addition, n2, instead of; may be used in the formula for emphasizing the recent Occurrence.
Due to limited number of buses and days involved, and the absence of any substantial accidental events, the daily segment norm of traversing time among all vehicles in May 1999 are arrived at by using an average method without further filtering. Since the month of May 1999 is the largest domain in this example, the norm of the segment in the month of May 1999 is calculated with average method after dropping the maximum value, and will be used as a base factor for calculating the Coefficient Index of Traversing Time. The calculation results are shown in Table 6.
Taking Segment P1-2 as an example, the maximum value "299" is dropped, and the arithmetic mean is taken of the remaining values as follows:(274+287 280+281)14 = 281 Since the Coefficient Index in this case is built from the traversing time (TS), its value will reflect the general status of the traffic condition. While a high CI value represents serious traffic congestion, the best traffic flow or condition is a CI value approaching zero.
With the completion of the building of this traffic route P1-5, two simple applications are possible. Let the Point of Occurrence be 14:23:23 of 10 May 1999 and its historical point of backward window extends to 13:23:23 of 10 May 1999 with Event count of less than S. Its Norm of Occurrence (as defined in Formula I above), and its Coefficient Index of Occurrence Norm with respect to the month of May 1999 as normalization factor, are shown in Fig. 7.
Let us defme the Occurrence CI of the Route P1-5-i as the arithmetic mean of Occurrence CI of P5-I, and mixed Occurrence CIs of Route P1-S.
Norm of Break Time (N-BT) at P1 per vehicle is defined as the product of the Occurrence Norm of Route P1-5-i and the Average Break Time per vehicle over the month of May 1999.
A first application of such a method involves Bus Arrival Schedule Reporting. At 14:34:23 of 10 May 1999, the information as shown in Table S can be shown or disseminated.
The Waiting Time for Arrival (rD-WA) in terms of minutes at P-PD points will be updated dw'ing the Event of Occurrence for every bus which arrives at any of the P-Pt) points, which activates the Events logged and stored into the system. Note that the negative values of Th-WA means that the Event drifts into past as time goes on.
A second application involves the prediction of a future time for the arrival of a bus. In this example, we are trying to give a prediction of the time of travel of a private car from P2 to P4 with a starting time of 14:34:23. Assuming that there is no material difference between a private car and a bus, except that a private car does not have any transition time for traversing a P-PD point. The traversing time is thus increased by a factor of FT-SC as defined in the following formuiae:- Formula 2 FT-SC (1(T-Arr - T-Dep))/Lj1 TS Where FT-SC stands for "Traversing Factor of Standard Automobile", and all transition events at all points except the starting point P1.
As for TD-TS (Traversing Time of Standard Car), Formula 3 TD-TS = FT-SC x ETS of Occurrence Where TS is Occurrence Norm of Traversing of segments concerned, or P2-S and. P3-4 in this
example,
For carrying out this invention, it is possible to arrange for a central processing unit, e.g. a main frame computer installed with appropriate application software, e.g. one traded under the name "Sybase Adaptive Server IQ'2" by Sybase, Inc.. The computer is also connected with a number of signal receivers and signal transmitters. Signals relating to the various data - 11- mentioned above may be transmitted to and be received by the signal receivers connected to the central processing unit. The central processing unit will then group and process the data, and store them for possible use.
Certain end users, e.g. bus companies, government departments (e.g. Transport Department), may require such data for statistical, analytical, and/or planning purposes, It is a1s possible for certain end users, e.g. a passenger on a bus, or an individual waiting for a bus at a bus stop, to make inquiries with the system, e.g. via his/her mobile phones. Such inquiries may include the estimated additional time for the bus he/she is travelling on to arrive at the next stop, or the estimated additional time for a bus of a certain route to arrive at a certain bus stop.
Requests for such information are received by the signal receivers connected to the central processing unit/computer of the system. The computer will then process the data according to the predeflnecj formulae, and the parameters inputted by the requesting end user(s). The results are then sent to the requesting end user(s) via the sial transmitters of the system, e.g. via a mobile phone system.
FB-DS Route j\p The main characteristics of a route of the FB-DS Route Type are the same as one of the PB-i'S Route Type, except that for a route of the FB-DS Route l)'pe, there is no fixed point of pickup and delivery, although the actual route is we-defined, same as a route of the PB-TS Route Type. A we-defined route run by mini-buses may fall within this route type. The basic format of raw datum packet is as follows:- (T-Log, L-ID-BS, D-MT1, D-MT2) where T-Log: Arrival Time or Departure Tune (as the case may be); L-ID-BS: Location of Base Stations set up along the road side of the route; D-MT,: Vehicle Identification D-M72: Vehicle Location Information (which is called "L-DS" after pre-processing) In this route type, as different from the case of PB-TS Route Type, the vehicle P-PD points may not coincide with or be near to the base stations. An extra raw data item, i.e. D-MT2, is thus required. The original form of this data item after capturing may be expressed in terms of longitude/latitude, or in terms of angle or degree format. However, after certain pre-processing work has been carried out, only a positive or negative value (called L-OS) is stored in the system. In this connection, L-DS represents the distance between the transportation vehicle in question and the base station, in which a positive value means that the vehicle has passed a certain base station, while a negative value means that the vehicle is still upstream of a certain base station and has yet to arrive.
The basic method and procedure to construct a cyber route of the FB-DS Route Type are essentially the same as that for constructing one for the FB-TS Route Type, although extra effort and overhead are required to handle the extra piece of raw data item, namely L-DS. In particular, in a route of the PB-TS Route Type, the Logging Datum Packet includes T-Arr and/or T-Dep. However, in the FB-DS Route Type, it is necessary to handle (T-Arr, L-DS) and/or (T-Dep, L-D$). When it comes to the handling of the traversing time (TD-TS), an effective traversing time (TD-TS-EFF) is defined as:- TD-TS-EFF = TD-TS x (I + L-DD/L-BS) where TD-TS-EFF is the effective traversing time; L-BS is the known segment length between two Base Stations; L-DD is the distance between two L-DS of two consecutive logging events; and TD-TS is the traversing time.
Of course, with the availability of more data as stored, more features or applications can be provided by a system with a FB-DS Route lSepe feature.
TLR-TS Route Type This Route Type is a mixture of PB-TS Route Type and FBDS Route I'pe, with the following important features:- exclusive usage of dedicated road or rail track no transition event between base stations requirement on tracking and reporting the vehicle status in details between base stations.
A train route is an example of such a Route Type. The datum packet format is as follows:- (T-Log, L-ID-BS, l)-MT1, D-MT2, D-MT3, D-MTO where T-Log: Arrival Time or Departure re (as the case may be) L-lfl-BS: Location of Base Station D- MT: Vehicle Identification D-MT2: Vehicle Location Information (which is called "L-D$" afier pie-processing) D-MT3: Vehicle speed (if changed during travelling) D-MT4: Passenger count on the vehicle (called "N-PA") D-MT1: goods information, etc. The method or procedure of handling this Route Type are basically the same as in the case of FB-DS Route Type, with the following exceptions:- the speed information allows cross checking of the validity of the information relating to the location of the vehicle, and reporting with a high degree of accuracy the location of the vehicle, at any point of time. Using the following formula: Formula 4 distance traversing time x speed to arrive at the distance, the result can be compared to location result obtained from the next logging datum packet. Unless the loading of passengers and goods carries tremendous weight and consumable factors, passenger count (D-MT4, or called "N-PA") and goods information (D-MT,) are mainly for reporting purposes, and should not affect The traffic status. The absence of any transition between base stations will simplify the handling process, as compared with PB-TS Route Type and FB-DS Route Type.
in this type of dedicated vehicle and track situation, some of the information/data may be exchanged with other database(s) via information networking in pre-defined protocol or pre-agreed format. Most of the conceptual technique used in the case of PB-TS Route Type and FB-DS Route Type can be applied for building a cyber route of TR-TS Route Type.
- as for the interpretation of the coefficient index, it should mean differently from the other Route Types, in particular since the rail track or road is dedicated for the use by relatively small number of vehicles or train on a single track.
WR-D$ Route Type The characteristics of a route of this Route Type are the combination of PB-TS Route Type, PB-DS Route Type and TR-TS Route Type. Its unique feature is that the vertical displacement distance from, say, the central line of the trafl3c route, is concerned. l'his kind of route includes highways with lines, wider riven for traversing, and narrow harbours fianctioning as waterway. The transportatiQn vehicle may be cars, ships, boats, etc. The datum format is as follows:- (T-.Log, L-ID-BS, 1)-Mi"1, D-MT2, D-MT3, ... D-MT1) where T-Log: Arrival Time or Departure Time (as the case may be) L-ID-BS: Location of Base Station D-MT1: Vehicle Identification D-MT3: Vehicle Location Information (which is called "L-DS" after pre-processing) D-MT3: Vehicle speed (if changed during travelling) D-MT4: Passenger count on the vehicle (called "N-PA") D-MT5: Vertical distance in question (also called as "L-FC" (Length from Centre of Route), or "N-CE' (Channel Number with the Route)) D-MT: goods infonnation, etc. If the driver of the vehicle is just using the L-FC/N-CIi information for reference purposes, and he/she has other means to avoid collision, such data item is attached to the Logging Datum Packet as a reference for the traffic status impact Alternatively, a group of Routes may be created with N-Cl-for L-FC in discrete and incremental manner as part of Route Identification key.
Most of the technique as used in the case of PB-TS Route Type and FB-DS Route Type can be applied to build up a cyber route of the WR-TS Route Type. In addition, most of the technique used in the case of PB-TS Route Type and PB-DS Route Type can also be applied to build up a cyber route of the TR-TS Route Type.
AC-DS Route Type The main characteristics of this type of route are essentially the same as those of the FB-TS Route Type, except that the ACDS Route 1pe is for a traffic route for an aircraft which is circulating in the air before landing onto an airport, or has just taken off from anairport. UPS or DUPS technology is ideal for tracking the position of the aircraft, Information (in the form of radio signals) relating to the location of the aircraft is transmitted and/or relayed to the airport control, In this case, there are only virtual base stations, instead of physical road side devices which are defined and positioned at certain points of the air traffic route. The basic format of raw datum packet is in the following form: (T-Log, L-ID-BS, D-MT1, D-MT2) where T-Log: Arrival Thne or Departure Time (as the case may be); L-ID-BS: Location of virtue Base Stations; D- MT1; Aircraft Identification D-MT2; Aircraft Location Information (which is called "L-DS" after pre-processing) As compared to the FB-DS Route Type, it should be understood that an aircraft does not have any points of pickup and/or delivery (P-PD) along its route, except the starting point and the end point. However, all original location inputs are in tenas of longitude/latitude, or in angle or degree format. After pit- processing, a positive or negative number (called "L-DS") will be arrived at and stored in the data repository. A positive L-DS value indicates that the aircraft has passed the base station in question, while a negative L-DS value means that the aircraft is still upstream of, and thus has yet to reach the base station in question. The method or procedure for building up the cyber route of the AC-DS Route Type is the same as those in the case of the PB-DS Route Type.
IM-CT Route Tvpç The main characteristic of this Route Type is its way of capturing traffic-related infonnation into the system. The original raw data are in the form of video image or acoustic wave, which is nondiscrete. Such data were scanned and captured by route side CCTV cameras or other kinds of image/sound sensing devices. The datum packet format is as follows:- (T-Log, L-ID-BS, D-MT1, D-MT P-MT3., D-MT) where T-Log: Frame Identification (also called cFIm) L-ID-BS: Location of the Base Station in question D-MT1: Number of Mobile Objects per Frame (tN-MPM") P-MI2: Number of Mobile Objects per line or lane per Frame D-MT3: Relative Distance between Mobile Objects per line or lane D-MT1: Any other kind of information about Mobile Objects.
The mobile object identification is not adopted as a mandatory component of the datum packet, On the other hand, Frame Identification (FI..TL) with reference to incremental I-Log is used as key. Analog-to-digital conversion and pattern recognition techniques are applied on the images and/or sound captured.
The kind of D-MT depends on the quality of the image or sound wave form and the capability of the pattern recognition equipment and technology. Some of these may be extracted, defined and shown as mobile targeted data in the above datum packet. Most of the conceptual technique as used in the case of the PB-IS Route Type and FB-DS Route 1pe can be used for building a IM-CT Route Type. Such a Route pe reflects the dynamic traffic status or flow on a macro level by counting mobile objects and/or keeping track of their relative variation.
UI Route Type The characteristic of this Route Type is that there is no corresponding physical route. This type of route is created by joining consecutive or adjacent segments of Routes which belong to the sane Route Type and same domain of normalization and CI (a higher value of which indicates a higher degree of traffic congestion). If two segments are selected from two routes, their physical cross-section has to exist and the traffic flow direction has to be the same. In addition, their Norm and CI must have the same nature or characteristics. -1$-

Claims (33)

  1. CLAIMS: I. A method of providing traffic-related data including the steps
    of:- (a) obtaining data of events occurring at a plurality of locations along at least one traffic route; (b) processing said data; and (o) providing the processed data to at least one end user.
  2. 2. A method according to Claim 1 wherein said data include at least a first component indicating the time at which said events occut
  3. 3. A method according to Claim 1 or 2 wherein said events include the time of departure of a transportation vehicle from one of said plurality of locations.
  4. 4. A method according to Claim 1 wherein said events include the time of arrival of a transportation vehicle at one of said plurality of locations.
  5. 5. A method according to Claim 3 or 4 wherein said data include at least a second component indicating the identity of the transportation vehicle(s) occasioning said event(s).
  6. 6. A method according to Claim 3, 4 or S wherein said data include at least a third rnmnnnnit imlirpitjnrs the lntaHnn at whkth qaid ventIqi nrriirI'q'
  7. 7. A method according to Claim 3 or 4 wherein said step (a) includes receiving signals from signal transmitting means associated with said transportation vehicle.
  8. 8. A method according to Claim 7 wherein said signal transmitting means includes an automatic vehicle locator.
  9. 9. A method according to Claim 7 or $ further including the step of transmitting said signals to a data processing unit.
  10. 10. A method according to Claim 3 or 4 wherein said step (a) includes manually recording the time at which said events occur.
  11. 11. A method according to Claim 10 further including the step of transmitting data relating to said recorded time to a data processing unit.
  12. 12. A method according to Claim 9 or 11 wherein said data processing unit comprises a computer.
  13. 13. A method according to Claim 3 or 4 wherein said step (a) includes importation of data from at least one database.
  14. 14. A method according to any of the preceding claims further including the steps of storing said data
  15. 15. A method according to any of the preceding claims wherein said step (b) includes grouping said data into a plurality of groups wherein each group comprises data of events oceuning within a first pre-detemiined time period,
  16. 16. A method according to Claim 15 wherein each of said groups comprises data of events occasioned by a particular transportation vehicle.
  17. 17. A method according to any of the preceding claims wherein said step (b) includes calculating the time that elapses between the occurrence of a first event occasioned by a transportation vehicle at a first of said plurality of locations and the occurrence of a second event occasioned by the transportation vehicle at a second of said plurality of locations.
  18. IS. A method according to Claim 17 wherein said second of said plurality of locations is next succeeding to said first of said plurality of locations.
  19. 19, A method according to Claim 17 or 18 further including the step of obtaining a norm value of time that elapses between the occurrence of said first event occasioned by said transportation vehicle(s) at said first location and the occurrence of said second event occasioned by said transportation vehicle(s) at said second location over said first predetermined time period.
  20. 20. A method according to Claim 19 wherein said step of obtaining said norm value includes normalizing the results of said calculation.
  21. 21. A method according to Claim 19 or 20 further including the step of obtaining a norm value of said norm values over a second pre-determined time period.
  22. 22. A method according to Claim 21 further including the step of generating a coefficient index relating to the occurrence of two events between two successive locations along said traffic route.
  23. 23. A method according to Claim 22 further including the step of generating, based on said coefficient index, a predictive time of occurrence of a future event at one of said locations along said traffic route.
  24. 24. A method according to any of the preceding claims wherein said step (c) includes providing said processed/generated data via a mobile phone system.
  25. 25. A method according to any of the preceding claims wherein said step (c) includes providing said processed/generated data by broadcasting.
  26. 26. A method according to any of the preceding claims wherein said step (c) includes - 20 - providing said processed/generated data to a database.
  27. 27. An apparatus for providing traffic-related data, including means for obtaining data of events occurring at a plurality of locations along at least one traffic route, means for processing said data, and means for providing the processed data to at least one end user.
  28. 28. An apparatus according to Claim 27 wherein said obtaining means includes signal receiving means for receiving signals transmitted from at least one transportation vehicle.
  29. 29. An apparatus according to Claim 27 wherein said obtaining means includes means for capturing sound and/or images.
  30. 30. An apparatus according to any one of Claims 27 to 29 wherein said processing means includes a data processing unit.
  31. 31. An apparatus according to Claim 30 wherein said data processing unit comprises a computer.
  32. 32, An apparatus according to any one of Claims 27 to 31 further including means for storing said data.
  33. 33. An apparatus according to any one of Claims 27 to 32 wherein said providing means includes broadcasting apparatus.
GB0428455A 2004-12-29 2004-12-29 A method and apparatus for processing traffic related data Withdrawn GB2421830A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120320013A1 (en) * 2011-06-16 2012-12-20 Microsoft Corporation Sharing of event media streams

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120320013A1 (en) * 2011-06-16 2012-12-20 Microsoft Corporation Sharing of event media streams
US9026596B2 (en) * 2011-06-16 2015-05-05 Microsoft Technology Licensing, Llc Sharing of event media streams

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