Incident Detection with Probe Vehicles: Performance, Infrastructure Requirements, and Feasibility

Copyright © IF AC Transportation Systems Chania, Greece, 1997

INCIDENT DETECTION WITH PROBE VEHICLES: PERFORMANCE, INFRASTRUCTURE REQUIREMENTS, AND FEASIBILITY K. F. Petty* A. Skabardonis** P. P. Varaiya*

*

Department of Electrical Engineering, University of California, Berkeley ** Institute of Transportation Studies, University of California, Berkeley

Abstract: In this paper we develop an incident detection algorithm based on information received in real-time from probe vehicles. We present a model which allows us to estimate the upper bound detection rate for a given density of probe vehicles. We demonstrate our algorithm on data collected from the 1-880 freeway in Hayward , California. We conclude that a probe vehicle-based algorithm is feasible , and it avoids some of the infrastructure problems facing loop-based algorithms. Keywords: Incident detection ; Probe vehicles; Radio Transmitter.

1. INTRODUCTION

and genetic algorithms (Abdulhai and Ritchie , 1997) . These algorithms have varying degrees of success with respect to detection rate , false alarm rate , and the mean time to detect an incident . A common problem with all of these algorithms is the high rate of false alarms that make them problematic to use in a large urban environment .

The cost of delay on freeways caused by nonrecurring incidents is significant. Some estimate that the cost will be $35 billion/year by the year 2005 (Lindley, 1986) . To reduce the impact of an incident a traffic management center (TMC) needs to quickly detect and remove it from the freeway. In this vein a large amount of research has been spent on the first part of the problem : quick detection. Since in a large urban envj.ronment the automation of this task is crucial, automatic incident detection algorithms have been the subject of study now for more than 20 years.

Besides the difficulties involved with implementing most incident detection algorithms , there are specific problems with loop-based systems. Loop detector-based systems are not easily expandable. Indeed , adding more measuring devices is extremely costly: the freeway needs to be shutdown , loops need to be cut in the pavement , and wiring and controller cabinets need to be installed. This makes it difficult to expand a loop based system on an operational freeway. This is a disadvantage because most freeways only have a small number of widely spread out loops which is not sufficient to detect incidents . Second , the wear associated with the loops in the road and the cabinets that control them being close to the freeway, and hence close to vandals, is excessive, resulting in a high failure rate (Bloomberg et al. , 1993) . Finally, even if the loop detectors are functioning , in order to

1.1 Difficulties with loop-based algorithms. Most research efforts in this area has dealt with trying to interpret information obtained from loop detectors. There have been many algorithms developed ranging over simple filtering (Stephanedes and Chassiakos, 1993) , pattern recognition (Tsai and Case, 1979) , catastrophe theory (Persaud and Hall , 1989) , and more recently neural networks (Ritchie and Cheu , 1993; Hsiao et al., 1994) 125

get accurate measurements of occupancy, a crucial parameter for most algorithms, the loop detectors need to be properly tuned , a process that in our experience (Skabardonis et al., 1995) takes a considerable amount of time and effort.

an algorithm can determine when it has passed an incident. Indeed, upstream of an incident vehicles should be in a slow-moving queue and when they pass the incident they should speed up to free flow speeds. By looking for this change in speed an algorithm should be able to determine the location, and possibly the severity, of an incident. This is clearly not the only detection algorithm. One could look for places where the vehicle rapidly decelerates from a free-flow speed to stop-and-go traffic. This could indicate the end of a the queue behind an incident. A more sophisticated method could use pattern matching on the speed profile of the vehicle.

1.2 Our approach.

In this paper we present an algorithm for detecting incidents on the freeway that doesn 't use loop detector based data. Instead , it uses data taken from probe vehicles that are driving around the freeway. These probe vehicles will be equipped with special radio transponders much like the transponders being proposed for automatic toll collection (indeed , they could be the same device) . These transponders will periodically report information like speed and/or position to local base st ations that will then relay it to a central TMC. The TM C will then be able to organize this data to develop a complete picture of the conditions on the freeway, including incident detection .

One should note that a probe-based algorithm is similar to a loop-based one in many ways. Much like loops, there are two ways that a probe-based algorithm can miss an incident. First , an incident could have little or no impact on the flow of traffic. Hence a probe vehicle could pass an incident and have little or no reduction in speed. Second, the incident could cause a fluctuation in the traffic stream but no probe vehicles would happen to pass through it to register it. This is exactly like a loop-based algorithm having loop detectors that are so spread out that they completely miss an incident.

The main implication of this approach is that expanding the system, geographically as well as in terms of sensing power, will be trivial. Geographically the system can be extended to any location with cellular phone base stations. Extra sensing power, which means having more vehicles with sensors, only involves convincing more drivers to install radio transponders in their vehicles - an easy task because they'll double as toll collection devices. It should be noted that this algorithm is specifically trying to leverage off of upcoming advanced traveller information system (ATIS) implementations.

On the other hand , there could be natural speed fluctuations in the traffic stream that look like the vehicle is passing an incident. The most common of these is a natural bottleneck like a change in the geometrics of the freeway or recurrent congestion. The approach that we take is we first filter the measurements from the vehicles with a standard moving average filter of width fw = 20 seconds. This suppresses the noise. We then take the derivative of the speed and look for accelerations above some threshold Q. Finally, we only note the places where the vehicle is accelerating through a certain speed , Vt. The idea behind Vt was to discard the normally large accelerations that occur in stop-and-go traffic. This combination of attributes should mark the location where the vehicle leaves the queue and accelerates to the free-flow speed. An example of this is given in Figure 1. Hence our parameter space is the pair (Q , vd . This double parameterizes our false alarm-detection rate curves.

1.3 Data used. The data that was used to develop and test our algorithm was collected from the 1-880 freeway in Hayward , California (Petty et al. , 1996) . That data set includes detailed loop detector data as well as probe vehicle data, and a complete incident database. The data were collected during the rush hour periods of 6:30-9:30am and 3:30-6:30pm. The probe vehicles in this study were recording their positions and speed every second on a laptop computer inside the vehicle. There were between two and four probe vehicles on the freeway at any one time. Note that the data that we used from the probe vehicles was not collected in real-time. We discuss the feasibility of collecting real-time data in Section 4.

3. RESULTS We ran this algorithm with various sets of (lw , Q, vd on three weeks of data with a total of 306 incidents. It is important to note that we did not pre-screen the incidents that we included in the study. We took everything that was in the incident database. This differs quite a bit from

2. MODEL The basic idea behind our model is that by observing the speed of a vehicle driving down the freeway 126

breakdowns occurred on a wide right shoulder they would have had a very small effect on the traffic stream and hence would have been difficult to detect .

Speed

A couple of things should be noted about the implementation of this algorithm: • There were sections of the study area that experienced recurrent congestion. Hence, in the same place on the freeway, at a certain time of day, our algorithm would produce many false alarms. Actually, if we were looking for when the vehicle passes a bottleneck then these wouldn 't have been false alarms at all! With a loop-based algorithm one would tune the detection thresholds for the loops near the bottleneck so that they are less sensitive to recurrent congestion. This is rather straightforward with loops simply because they don't move. Although we could have modified our probe-based algorithm to be less sensitive on various parts of the freeway during certain times of the day, we didn 't . • The algorithm was making a determination as to whether there was an incident or not for every probe vehicle for every second. Hence, for a typical day this resulted in a huge number of decisions. Despite this, the number of false alarms per hour-mile of the freeway range from x to y. • An important consideration is the number of probe vehicles on the freeway. If every vehicle was a probe vehicle then we should never miss a detectable incident. Whereas with only a few probe vehicles it is possible to miss an incident simply because the density of probes was not high enough. In this study we had between probe vehicle head ways between 6 and 8 minutes which translates to probes being approximately 0.08% to 0.1% of the vehicles. While it is obvious that the higher the percentage of probe vehicles the better the detection rate , the sensitivies are not clear. Figure 4 gives a striking example of the difference in detection-false alarm rates for police ticketing incident with 3 versus 4 probe vehicles. Note that the detection rate almost doubles. In most of the other cases, there is not that much of a pronounced difference.

Distance Acceleration

I

incident

Distance Fig. 1. Model for incident detection b~ed on probe speed . many incident detection studies (Abdulhai and Ritchie, 1997). Quite a few studies try to find major incidents on the freeway by hand and then try to detect only those incidents. We believe that one should be using an incident detection algorithm to determine what type of incidents are detectable. Any pre-screening of incidents is going to bias the results. The basic results for the incident detection algorithm are given in Figure 2. That figure shows the detection rate vs. the false alarm rate. The key in the lower right-hand corner of the figure indicates the type of incidents this graph is for. In this example we have any location on the freeway (right-hand side, left-hand side or in-lane), any type of incident (accident , breakdown , or police ticketing), and any tow truck action (either a tow truck showed up or it didn 't) . For all of the incidents it is clear that the detection rate is rather low. But when you break this into the various categories of incidents - police (CHP) ticketing, accidents, and breakdowns - as in Figure 3, the results are quite a bit better. The algorithm can detect approximately 70% of the accidents on the freeway. The detection-false alarm curves are the same for the police ticketing and breakdowns. This is quite significant in that it suggests that ticketing incidents cause a significant interruption in the traffic stream . From Figure 3 it is clear that the overall detection rate is being pulled down by the fact that the algorithm .is trying to detect a large number of breakdowns. Since most of these

A final interesting comparison is given in Figure 5. This figure compares the two extremes in detection difficulty: a right-shoulder breakdown , which should be very hard to detect, and an in-lane or left-shoulder accident, which should be very easy to detect. Figure 5 shows that our intuition holds true with the algorithm able to detect up to 90% ofthe in-lane accidents. It can only detect at most 40% of the right-shoulder breakdowns. 127

Incident detection results (3 <= cars < 5)

0.9 0.8

0.7 0.6

CD

"§ c

o

0.5

:g CD

«i

o

0.4

0.3 0.2 0.1

o ~----~----~----~--~~~----~----~----~----~~----~--~ o 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 0.0014 0.0016 0.0018 0.002 False alarm rate

Fig. 2. Overall results of probe-based incident detection algorithm . Each data point corresponds to a different pair of thresholds (a , Vt) . To convert false alarm rate to false alarms per hour per mile then multiply by 977.

;= : ~::: : g:~~: Z;= ~~I :

0 .9

•

,§

¥

.3

0.9

0.8

0 .8

0.7

0 .7

0.0

0 .0

•

O.S

,§

0.4

!

¥

0.3

,. . .

,

O.S 0 .4 0 .3

02

02

0.'

0 .'

0 0

0.0002

o.~

0.0006

O.CICX:.

0.001

0.0012

F.aa."rmrat.

0.0014

0.0016

0,0)1'

0

0.002

0

0.0002

O.OCJI:W.

0..000&

O.CJXl8 0.00'1 0.0012 F. . . alalmral.

0.0014

0.0016

0.(101'

0 .C02

Fig. 4. Results of probe-based incident detection algorithm on ticketing incidents with different number of probe vehicles.

Fig. 5. Results of algorithm on In-lane accidents and right shoulder breakdowns - the two extremes in detection difficulty.

4. FEASIBILITY

ATIS implementations. To achieve this we propose to use vehicles equipped with radio transponders that will communicate with existing cellular base stations via the standard Cellular Digital Packet Data (CDPD) protocol (Garg and Wilkes, 1996) . The CDPD protocol is a way for individuals to communicate digital data to cellular phone base stations. This technology is currently used , for example, by shipping companies that want their trucks in the field to communicate their locations to their corporate headquarters (to perform scheduling) . The cellular phone base stations

When we developed the detection algorithm we used off-line data. But for a real deployment this data needs to be collected and transmitted to the TMC in real-time.

4.1 Data collection method

Our primary goal is to use as much off-the-shelf technology as possible and to leverage off of other 128

Incident detection results (3 <= cars < 5)

0.9 0.8 0.7

Q)

0.6

~ c 0

°u

0.5

Q)

Q) Cl

0.4 0.3 0.2 0.1 0 0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

False alarm rate

Fig. 3. Results of probe-based incident detection algorithm by type. The numbers in parenthesis in the key are the number of incidents. Each data point corresponds to a different pair of thresholds (Q , Vt) . 4.2.l. Data requirements.

which support CDPD are ubiquitous in all large urban environments. Once the data is transmitted from the vehicle to the base station it will travel via data network to the TMC where it will be integrated with other data to form a complete picture of the conditions on the freeway. By designing the system this way we are essentially trying to "piggy back" on top of already existing technologies.

In our algorithm we knew the location and speed of every vehicle at each second. Transmitting this every second via a wireless link would be cumbersome. Instead a vehicle could batch up their speeds over the last 30 seconds, compress them and send them with their current location. This would allow a central computer to reconstruct the state of the freeway and then run the incident detection algorithm . Better yet , a vehicle could be running the incident detection algorithm itself and only reporting it 's location to the TMC when it detects an incident. While this would definitely minimize the data being transmitted via the wireless link, it would present other practical problems. For example, how would one upgrade the incident detection algorithm in all of the probe vehicles? For simplicity we will assume that the probes batch their speeds and transmit every 30 seconds. This will translate to 30 samples of speed data, 1 location value, and then the various headers on the data. Allowing for generous overhead this gives us about 200 bytes for every probe vehicle every 30 seconds.

4.2 Feasibility of real-time data collection The main difficulty in implementing this scheme is that it is hard to relay all of the data to the TM C . The largest potential bottleneck is the radio link from the probe vehicles to the base station. While the other bottlenecks - the wired data path from the base station to the TM C , and the speed of the computer that will assemble all of the information - are important, the wireless link is unique to this implementation and hence we won 't discuss the others here. The questions that need to be answered are: 1) how much data is each probe going to be transmitting to the base station , 2) how many probes are there going to be in each cell and hence talking to one base station , and 3) what 's the capacity of a base station?

4 .2.2 . Density of probe vehicles. The number of probe vehicles that are willing to be outfitted with these special radio transponders 129

Bloomberg, 1. D., V. W. Bacon and A. D. May (1993) . Freeway detector data analysis: smart corridor simulation evaluation. Technical Report UCB-ITS-PWP-93-1. Institute of Transportation Studies, University of California, Berkeley. Garg, V. K . and J . E. Wilkes (1996) . Wireless and Personal Communications Systems. PrenticHall , Inc. Hsiao, C ., C. Lin and M. Cassidy (1994). Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents. Journal of Transportation Engineering 120(5), 753-772 . Lindley, J . A. (1986). Qualification of urban freeway congestion and analysis of remedial measures. Technical Report RD/87-052. FHWA. Washington, D.C. Persaud , B. N. and F. L. Hall (1989). Catastrophe Theory and Patterns in 30-second Freeway Traffic Data - Implications for Incident Detection . Transportation Research - A 23A(2) , 103-113. Petty, K . F. , H. Noeimi , K. Sanwal , D. Rydzewski , A. Skabardonis, P. Varaiya and H. Al-Deek (1996). The Freeway Service Patrol Evaluation Project: Database , Support Programs, and Accessibility. Transportation Research, Part C (Emerging Technologies) 4(2) , 71-85 . Ritchie , S. G. and R. L. Cheu (1993). Simulation of Freeway Incident Detection Using Artificial Neural Networks. Transportation Research C 1(3) , 203-217. Skabardonis, A. , H. Noeimi, K. Petty, D. Rydzewski , P. P. Varaiya and H. AI-Deek (1995) . Freeway service patrols evaluation. Technical Report UCB-ITS-PRR-95-5. Institute of Transportation Studies, University of California, Berkeley. Stephanedes, Y. J. and A. P. Chassiakos (1993 ). Freeway Incident Detection Through Filtering. Transportation Research - C 1(3) , 219233. Tsai , J. and E. R. Case (1979) . Development of freeway incident-detection algorithms by using pattern recognition techniques. Tran sportation Research Record (722) , 113-116.

is a tricky question. As radio-based automatic toll collection becomes more prevalent this number will surely grow . For now we can safely assume that we won 't have any more than 5% of the vehicles being probes. In a 2 km cell with 5 lanes of traffic in each direction this translates to about 20 vehicles being probe vehicles. Hence the capacity needed is: . 200 bytes CapacIty needed = 30 x 20 veh sec 1066 bits sec

(1) (2)

4.2.3. Capacity of a base station.

A base station can have a variable number of radio channels reserved for CDPD use. While the number of channels available for CDPD depends upon the many factors that we won 't go into here, it suffices to say that the capacity of a single CDPD channel is approximately 19.2 Kbps (Asawa and Stark, 1995). Hence, a single CDPD channel could more than accommodate the information needed to run this probe-based incident detectio:t:I algorithm.

5. CONCLUSIONS We conclude that it possible to detect certain types of incidents with a probe-based incident detection algorithm . Through a straightforward calculation we determined that it was technically feasible to deploy a probe vehicle-based incident detection system with existing technology. The difficulty with loop detector based systems, including the difficulty in maintaining them , the high cost of expansion , and the relatively sparse coverage, coupled with the recent deployment of new vehicle-based radio transponder applications will pave the way for probe based incident detection systems. Future work will entail testing pattern matching algorithms with the data to determine their effectiveness.

6. REFERENCES Abdulhai , B. and S. G . Ritchie (1997). Development of a universally transferable freeway incident detection framework . Transportation Research Record. Asawa, M. and W .E. Stark (1995). Throughput analysis of cellular digital packet data with applications to intelligent transportation systems. In : 1995 IEEE 45th Vehicular Technology Conference. Vol. 2. IEEE. Chicago, IL . pp. 564-568 . 130