Chapter 3 Traffic Information Generated from LBS-based Applications
3.3 Traffic information generation
3.3.1 Traffic information spot (TIS)
Traffic information transformation module transforms the cleaned raw data into traffic information by integrating the urban road network database in GIS. Each uplink packet of the OBU can be transformed into a traffic information spot (TIS) because the information contained in the uplink packets includes location, moving speed, moving direction, and the state of the vehicle. By integrating the road network database with GIS, the coordinates of the GPS position of a vehicle can be interpolated to nearest address. Thus traffic information can be generated by transforming the uplink packet into TIS. A TIS Sk(Oij, V, D) transformed
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from the OBU uplink packet Uk of a vehicle, as illustrated in Equation (3.1), consists of object id (Oij), speed (V), direction (D) of the vehicle when it communicates with the LBS backend system at time t and location (x, y), where Oij is a spatiotemporal network object spatially indexed by network object id i (transformed from the location to address interpolation) and temporally indexed by time zone j (transformed from timestamp t).
)
In addition to TIS which indicates the traffic status at one fixed point, a vehicle journey represents the tracks of a vehicle starting from its origin to the destination. A vehicle journey, which partially reflects the traffic demand in the urban network, is a collection of consecutive TISs of a vehicle and can be extracted from the LBS raw data. For example, ‘dispatched’
state journey extracted from TDS consists of a set of TISs which starts from the dispatched location to the customer’s location, and ‘occupied’ state journey starts from the customer’s location to their destination.
Vehicle journey generation module extracts ‘meaningful’ journey (indexed by k) from the raw data collected from the LBS-based applications, for the TDS example, taxi that is in the ‘dispatched’ or ‘occupied’ states, where a journey of a vehicle consists of a set of consecutive TISs reported from the origin S1 of the vehicle to its destination Sn, as illustrated in Equation (3.2).
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Jk=<S1,..,Sn> … (3.2)
3.3.3 Traffic network snapshot (TNS)
In order to enhance the global traffic network performance, network administrators have to realize the traffic status of the whole network. Traffic network snapshot (TNS) provides a global view of traffic status in a time period for the traffic network, which is indexed by spatial and temporal dimensions. Spatial domain groups the TISs by spatial area of the network object (e.g., link) and temporal domain groups the TISs by time zone (e.g., 15 minutes). Let a TNS be composed of a set of a spatiotemporal network objects during a 15-minute period. Therefore, the traffic status of urban network in morning workday peak hour (7~9 AM) includes eight snapshots. All the spatiotemporal traffic network objects and snapshots generated in this phase are stored in TIDB (traffic info. database) as the data source of the subsequent data mining processes.
TNS can be easily presented by map based user interface for reflecting a short period of network status by the assistance of the GIS. As illustrated in Figure 3-2, there are three continuous TNSs of an urban network from 7AM to 7:30AM, where the objects marked as yellow lines indicate the congested links. With the knowledge of global network traffic status and continuous traffic status variations which can be presented by continuous TNSs, the network administrators can decide which traffic assignment actions to be the best action by their domain expertise.
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Figure 3-2 examples of three continuous TNSs (from 7AM~7:30AM)
3.3.4 Traffic information generation for freeway
The focus of traffic information for the freeway area is different from that for the urban area; travelers are more concerned about the traffic event messages and ramp to ramp traveling time than the route path choice in the freeway. On the contrary, in the urban area, travelers are concerned about the route path choices in order to avoid the congestion. Since the topology in the freeway is much different from that in the urban network, different sampling strategies are used to generate the traffic information depending on the location of the traffic information in freeway area or urban area. For example, if a vehicle is equipped with OBU or STA (smart traffic agent, discussed in the next chapter) traveling through the freeway, a series of samples uploaded from the same OBU/STA are collected by the backend traffic information center (TIC), and each sample can be transformed to the format of (Mx, Tx), where Mx indicates the mileage and Tx indicates the uploaded timestamp. By selecting the entry sample (Ms,Ts) and exit sample (Me,Te) of a vehicle, the average traveling speed (Vs,m)
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between these two ramps can be calculated by (Me-Ms)/(Te-Ts), which is regarded as a case.
For any two ramps k and j, the overall average travelling speed can be calculated by the arithmetic mean value (Vk,j) of for all the cases of (k, j) as shown in Equation (3.3).
3.3.5 Traffic information generation for urban network
Urban network consists of a set of network objects, each of which is either a link or an intersection where traffic congestions occur on some network objects in which the traffic demand cannot be fully serviced. The traffic status of the network which constitutes the network can then represent the traffic status of urban network.
(I) Traffic information of a link
The average speed of a link in a temporal period can be calculated by arithmetic mean value shown in Equation (3.3) of all the samples spatially falling in the link and temporally falling in the temporal range. However, in urban area, it seems not reasonable to represent link traffic status by the average speed for all the links because the service levels of different road grades are different. As shown in Table 3-1, the service levels of the three road categories in urban area of Taiwan are defined from level A to F [CLO01]. The link traffic information can be transformed to the service level by mapping the average speed to the road grade and service level defined in Table 3-1. For example, average speed of 30km/hr indicates the traffic status is good (level B) in street (grade III), but slight congestion (level D) in the expressway
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or arterial (road grade I).
Table 3-1 Service level classification for three categories of road in Taiwan
Road grade Ⅰ Ⅱ Ⅲ
Free flow speed 55 (kph) 45 (kph) 40 (kph) Service level Avg. speed (kph) Avg. speed (kph) Avg. speed (kph)
A (90%) ~51 ~43 ~33
B (70%) 51~39 43~32 33~25
C (50%) 39~34 32~27 25~20
D (40%) 34~29 27~23 20~16
E (33%) 29~21 23~17 16~10
F (25%) 21~ 17~ 10~
(II) Traffic information of an intersection
On the other hand, the average intersection delay can represent the traffic information for the intersection, and the delay between two consecutive links, which is mostly caused by signal delay and queuing delay and can be classified by TD, LTD and RTD patterns according to the three possible directions from one link to the next link. Equation (3.4) shows the general format of intersection delays (TD/LTD/RTD), where P is the pattern type, SOid and SIid are the two consecutive links at the intersection where the vehicles leave out the link SOid
and come into the link SIid, Tid is the temporal id, Davg is the average delay time of this intersection, and Sup, Con are the support and confidence of the pattern, respectively.
[TD/LTD/RTD]: (P, SOid, SIid, Tid, Davg, Sup, Con) … (3.4)
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For example, (‘RTD’, ‘L1’, ‘L2’, ‘W,P’, 40, 0.2%, 75%) represents that in the peak hours of workday, it takes 40 seconds to do a right turn from link ‘L1’ to link ‘L2’, and the support is 0.2% , confidence is 75%. Intersection delay patterns can be discovered by sequential pattern mining or spatial and temporal sequence mining on all samples of intersection delay in a journey containing two consecutive samples with different links in the historical traffic information database. Figure 3-2 shows an example of RTD pattern: a probing vehicle driving north and then turning right to east, it reports TIS at location A of Link La and consecutively reports TIS at location B of Link Lb. The symbols of the TIS format (T,L,X,Y,D,V) in Figure 3-3 stand for timestamp (T), link id (L), coordinates (X,Y), direction (D) and speed (V), respectively. The distance da, db in Figure 3-2 stands for the distance from A or B to the intersection of links La or Lb respectively. Assuming that in the short period time interval between Tb and Ta, the vehicle is driving at the speed of Va at link La and Vb at the link Lb. Then the right turn delay (RTD) time from La to Lb can be estimated by subtracting travel time of da and db from elapsed time between two TISs (Tb-Ta).
Figure 3-3 Intersection delay example: RTD
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The status of each intersection delay in an intersection is also classified into six service levels (A~F) by normalizing each intersection delays, i.e., by equally dividing the intersection delay samples into six levels from the historical traffic information database. With the traffic status of all the links and intersections, the whole network status can be easily represented on map by coloring the objects in the user interface.
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