Origin Destination (OD) matrices are often estimated based on expensive travel surveys. An alternative data source is counting trains with either infrared equipment (beams) or automatic video camera passenger detection systems. This is also an expensive data source, e.g. 0.5 million Euro for 8 trains in Copenhagen. Most modern trains measure the weight of the trains compared to an empty base situation to calibrate the brakes. The Copenhagen S-trains collects this data in the train computer, which is connected toa central server. The Company considered using this information for OD matrix estimation. Manual counts were compared with the counting equipment. Entering and exiting passengers must equal from the start to the end station. It was shown, that the variance of the observations was approximately equal to manual counts. However, the beams had the tendency to have a larger variance per passenger at stations with many entering or exiting passengers, which introduced a systematic error along a given train run. Errors also accumulated along train runs. When counts were balanced to fit along a certain train runs, this meant that the average absolute error is somewhat larger in the middle of the run. The weighing trains measure the passenger weight. A small sample of counting trains is used to estimate the weight per passenger. This may vary over the year and geographically. By comparing manual and automatic counts with the weights it was possible to evaluate the stability of passenger weights. In operation, the automatic counting trains are used to estimate the average weight per passenger in a giventime-period along a given rail line. The data analyses revealed that passenger estimations based on weighing trains had a higher variance than the counting trains. The train data collection system thus consists of about 6% counting trains that are used to estimate boarding and aligning patternsas well as weight per passenger, and a 100% sample of weighing trains. The observations and their variance are fed into the Multiple Path Matrix Estimation Method. This method tries to fit matrices assigned to the networkas well as possible to the counts at the same time as trying to change a base matrix as little as possible. If the counts are consistent, then the first deviation will become zero. MPME uses stochastic user equilibrium traffic assignment model, where the counts along routes are used to estimatethe matrix according to the likelihood that the route is used. A simulation method was used for validation. Here a base matrix was assumed. This was simulated to have seasonal, weekly and daily variation. Hence a "true" year was simulated. For each day, the simulated true matrix was assigned onto the network. The weighing information was simulated according to the known uncertainty distribution of the weighing system, and a sample of the 6% counting trains was simulated as well. Then MPME was run for each day, and this was repeated for a number of years for different base matrices (ranging from the true November matrix to a uniform matrix). As a comparison,the same simulation approach was used to evaluate the existing system. The tests showed that the error of the annual passenger estimates based on the new approach was reduced from about 3% to about 0.01%. A further benefit is that the new system estimated daily matrices in 10 min segments. Thisprovides information on weekly and seasonal variation of passenger flows.For the covering abstract see ITRD E145999
Abstract