# University of Twente Student Theses

## Data fusion for instantaneous travel time estimation : loop detector data and ETC data

Do, M.
(2009)
*Data fusion for instantaneous travel time estimation : loop detector data and ETC data.*

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Abstract: | With the emergence of Advanced Traveler Information Systems (ATIS), it is possible to provide various kinds of information to road users. Travel time is one of the most understood measures for road users. By providing reliable travel time estimates it is possible to influence road users’ route choice and travel behavior, hence improving the performance of traffic networks. The goal of this research is to develop a data fusion between loop detector data and ETC (Electronic Toll Collection) data to make more accurate real-time (instantaneous) travel time estimates on expressways. Unlike previous attempt of data fusion, this research will not use historical and statistical analyses for data fusion. By relying on statistical methods, the models fail to take traffic engineering principles into account. And by using historical data the developed models can only be applied at locations where historical data is available. Problem here is when there is a change in the traffic network, it needs to be examined if historical data before the change can still be used as input for the model. Loop detectors are the most common vehicle detectors for freeway traffic, these sensors continuously measure traffic speed and flow. This makes detectors very suitable for instantaneous travel time estimation, providing expected travel time to vehicles entering the expressway. But loop data does not provide an accurate image of the traffic conditions. This is because the detectors only collect data at point-locations and not over the entire road. ETC data on the other hand gives measured travel times over the entire road, vehicles’s location and times are being registered when they enter and leave a toll area. Disadvantage of this data is that it becomes available after that the travel time has been realized, while the goal is to provide estimations to vehicles at the beginning of their travel. The study area for this research is the metropolitan expressway (MEX), route #4, leading from Takaido towards the Tokyo ring (Miyake-zaka Junction). Length of the area is about 14 km. Since the detector placement in this study area is very dense, about every 100 meters, for the data fusion not all detector data will be used. Only data from 4 sections will be used, this will make the research more representative for the European and American road conditions (concerning detector density). The Miyake-zaka Junction connects route #4 with the ring-road in Tokyo, during peak hours this ring is heavily congested. Travel time over this route in normal (free-flow) condition is about 6 minutes, while during congestion the travel time can exceed the 20 minutes. The further away from the ring, the less the congestion gets. This makes route #4 an interesting study area. Since it goes towards a congested area, there will be various traffic conditions on the route. For this area aggregated loop data (speed, flow, and occupancy) for each segment for every 5 minutes is available. Data from each segment is aggregated from three dual-loop detectors. Pulse data from the individual detectors and data per lane was not available. As for ETC data, entering and exiting time and locations for individual vehicles were registered. All data was from the period of July 1st 2006 till July 7th 2006, ETC market penetration at this period was about 60%. To evaluate how accurate estimates could get based on loop data only, a time slice model was examined. This model is more suited for historical travel time analyses, because for each segment this model determines a vehicle’s entering time and based and that data from the corresponding time-interval us used for estimating travel times. By using the data corresponding to the same time-interval a vehicle is traversing a segment, this model takes speed variations over time into account. In case this model is applied for real-time applications, a delay has to be taken into account. Just like ETC data, this model gives travel times after the actual travel time has been realized. Throughout this research several fusion concepts were examined. The first one examined was a model running two models parallel, the Extrapolation and the Nam and Drew. By integrating ETC data previous time-intervals were evaluated and based on the previous intervals an estimate for the current interval would be calculated with the estimates of the Extrapolation model and the Nam and Drew model. The corrections for this model are illustrated in Table 1. Parts of the travel time estimates graphs and ETC graphs are plotted, identification of the situation, error determination, and correction are demonstrated. The yellow dotted line is the ETC data, the green line is the Nam and Drew model, the red line is the Extrapolation model, the blue dot is the corrected estimation. 1. Rule #1, the last two intervals with ETC data available were overestimated by one model and underestimated by the other model. The travel time estimate for the current interval is assumed to be in between of the two models. The model with the lowest output will be corrected upwards based on errors in previous intervals. 2. Rule #2, the last two intervals with ETC data available were underestimated by both models. Current estimate is assumed to be underestimated and the Extrapolation model will be corrected upwards based on errors in previous intervals. 3. Rule #2, the last two intervals with ETC data available were overestimated by both models. Current estimate is assumed to be overestimated and the Extrapolation model will be corrected downwards based on errors in previous intervals. The second concept only uses one existing estimate model as basis, the Extrapolation model. The ETC data is used to evaluate the error in previous time intervals. Based on the current travel time estimate trend, either ascending or descending, travel time would be corrected assuming that the previous error is still present in the current interval. Illustrations of the correction methods of the second model are shown in Table 2. The yellow dotted line is the ETC data, the red line is the Extrapolation model, the blue dot is the corrected estimation. 1. The last two estimates by the Extrapolation model are ascending. Current estimate is assumed to be underestimated and will be corrected upwards based on errors in previous intervals. 2. The last two estimates by the Extrapolation model are descending. Current estimate is assumed to be overestimated and will be corrected downwards based on errors in previous intervals. The last concept examined in this research is very similar to the second. A moving average on the Extrapolation model was introduced to stabilize the output, which is used to identify traffic conditions. Without the moving average, the output was too instable to be used for identifying traffic conditions. Because of time constrains only one correction rule was made for this model, see Table 3 for illustration. 1. The last two estimates by the Extrapolation model were first ascending followed and than descending. Current estimate is assumed to be overestimated and will be corrected downwards based on errors in previous intervals. Out of the three examined concept, the first and the last concepts are successful fusions. The first method was only tested with all loop detectors as input for the instantaneous model. It quickly turned out that running two models in parallel complicates the model a lot. And because of time constrains this model was not further examined. Average error was decreased by only a few seconds. The second model turned out to be an unsuccessful fusion. For the second model only data from four detectors was used. This resulted in very varying output from the Extrapolation model. The varying output was not suitable for traffic condition identification, which is the reason why this model didn’t improve travel time estimates. The third model is a further developed version of the second model. By introducing a moving average, the output of the Extrapolation model was stabilized and became suitable for traffic condition identification. It turned out that applying the moving average improved travel time estimates already. Because of time constrains, only one condition and correction rule was completed for this model. Estimates for this model are expected to become more accurate when the condition and correction rules are further developed. For now average error is decreased by about 10 seconds. For further research the condition and correction rules need to be developed further, for example taking more intervals into account. This research has only demonstrated a fusion method that can be successful. Travel time estimations by instantaneous models depending on loop data clearly have systematic errors, correcting these errors without statistical methods is possible. |

Item Type: | Essay (Bachelor) |

Clients: | University of Tokyo, Tokyo, Japan |

Faculty: | ET: Engineering Technology |

Subject: | 55 traffic technology, transport technology, 56 civil engineering |

Programme: | Civil Engineering BSc (56952) |

Link to this item: | https://purl.utwente.nl/essays/74775 |

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