Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence
Abstract
:1. Introduction
2. Literature Review on FCD Usage
2.1. Path Choice and Route Attribute Evaluation
2.2. O-D Demand Flow Estimation
3. Methodology
- Car trip detection: according to some predefined rules, this stage aims to detect the activity stops performed by each sampled vehicle; therefore, the individual trips (with origin and destination) undertaken by each surveyed vehicle can be obtained;
- Sample O-D matrices; according to study area zoning, the origin and destination of each sample vehicle trip is identified; then, through an aggregation procedure, the sampled O-D vehicle trips are then merged in order to obtain the daily (or timely) O-D matrix;
- Expansion to the universe of investigation; in this step, the sample daily (or timely) O-D matrices need to be expanded to the universe of observation in order to obtain the daily/timely-dependent vehicle O-D matrices of the study area. This step can be considered the core of the procedure, given that the statistical significance of the sample needs to be determined.
3.1. Car Trip Detection
3.2. Sample O-D Matrices
3.3. Expansion to the Universe of Investigation
- is the sample estimate of the variance of the variable :
- αk is the sampling rate in the k-th stratum.
4. Application to a Real Test Case
4.1. The Study Area and Available Data
4.2. Car Trip Detection
4.3. Sample O-D Matrices
- The predominance of intra-province trips;
- The O-D matrices reproduce quite well the spatial distribution revealed by ISTAT with very limited daily variation.
4.4. Expansion to the Universe of Investigation
4.5. O-D Matrix Validation
4.6. The Road Ahead and Open Research Challenges
4.6.1. Sample Size
- is the mean of VT for province o;
- is the estimated mean of the travelling vehicles VTo;
- is the quantile of the distribution (under Prob = 0.95 equals 1.96);
- is the sample standard deviation for VTo;
- So is the number (days) of observations for VTo.
- is the number of cars that should be monitored for O-D pair od according to sampling statistics data in day s;
- is the average number of trips made by vehicles between or within provinces during the observation day s;
- is the variance of number of trips made by vehicles between or within provinces during the observation day s;
- is the coefficient of variation of number of trips made by vehicles between or within provinces during the observation day s;
- is the average number of trips made by one vehicle during day s;
- is the detected (revealed) flow between zone o and zone d between or within the provinces in survey (observation) day s;
- is the detected number of origin zones in day s for province o;
- is the detected number of destination zones in day s for province o.
4.6.2. Sampling Days
- is the number of survey days required for the flows between zones (provinces) o and d;
- is the average value of flows on O-D pair od within S days;
- is the variance of flows on O-D pair od within S days;
- is the average O-D flow between zone (province) pair od for the survey day s.
4.6.3. Example of Application to the Veneto Region
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Province | Area, [km2] | Inhabitants | Males | Females | Number of Cars | Average No. of Vehicles per Inhabitant | Sampled Vehicles * |
---|---|---|---|---|---|---|---|
Belluno | 3678 | 204,900 | 49% | 51% | 135,261 | 0.660 | 918 (0.7%) |
Padua | 2141 | 936,740 | 49% | 51% | 603,290 | 0.644 | 7503 (1.2%) |
Rovigo | 1789 | 236,400 | 49% | 51% | 159,231 | 0.674 | 420 (0.3%) |
Treviso | 2477 | 887,420 | 49% | 51% | 588,052 | 0.663 | 5875 (1.0%) |
Venice | 2463 | 853,552 | 48% | 52% | 471,324 | 0.552 | 3274 (0.7%) |
Verona | 3121 | 922,821 | 49% | 51% | 614,838 | 0.666 | 3870 (0.6%) |
Vicenza | 2722 | 863,204 | 49% | 51% | 577,339 | 0.669 | 7298 (1.3%) |
Total | 18,391 | 4,905,037 | 49% | 51% | 3,149,335 | 0.642 | 29,158 (0.9%) |
Sampling/Surveyed Days | No. of Observations in the Database | ||
---|---|---|---|
Sampled Vehicles Travelling | Trip Description | Trip Details | |
15.10.2018 | 16,760 | 66,639 | 797,408 |
22.10.2018 | 16,674 | 67,057 | 794,775 |
07.11.2018 | 16,958 | 69,447 | 799,604 |
15.11.2018 | 17,139 | 71,304 | 883,329 |
23.11.2018 | 17,427 | 74,548 | 924,729 |
Average | 16,992 | 69,799 | 839,969 |
Province of Vehicle Registration | Survey Day | Average No. of Cars Sampled | Standard Deviation, Cars | ||||
---|---|---|---|---|---|---|---|
15.10.2018 | 22.10.2018 | 07.11.2018 | 15.11.2018 | 23.11.2018 | |||
Belluno | 449 | 441 | 449 | 461 | 441 | 448.2 | 7.33 |
Padua | 4421 | 4412 | 4475 | 4545 | 4412 | 4453.0 | 51.64 |
Rovigo | 236 | 236 | 238 | 235 | 236 | 236.2 | 0.98 |
Treviso | 3760 | 3747 | 3804 | 3832 | 3747 | 3778.0 | 34.17 |
Venice | 1519 | 1514 | 1513 | 1566 | 1514 | 1525.2 | 20.51 |
Verona | 2249 | 2233 | 2271 | 2288 | 2233 | 2254.8 | 21.67 |
Vicenza | 4126 | 4091 | 4208 | 4212 | 4091 | 4145.6 | 54.13 |
Unknown location | 955 | 945 | 961 | 985 | 945 | 958.2 | 14.73 |
Extra Veneto region | 1377 | 1337 | 1296 | 1389 | 1337 | 1347.2 | 33.06 |
Total | 19,092 | 18,956 | 19,215 | 19,513 | 18,956 | - | - |
SWod | Verona | Vicenza | Belluno | Treviso | Venice | Padua | Rovigo | Total |
Verona | 97.05% | 2.18% | 0.00% | 0.05% | 0.08% | 0.42% | 0.22% | 100.00% |
Vicenza | 1.28% | 94.37% | 0.08% | 1.08% | 0.14% | 3.02% | 0.03% | 100.00% |
Belluno | 0.01% | 0.24% | 96.83% | 2.62% | 0.16% | 0.14% | 0.00% | 100.00% |
Treviso | 0.04% | 2.01% | 0.57% | 89.76% | 5.43% | 2.18% | 0.01% | 100.00% |
Venice | 0.13% | 0.48% | 0.04% | 8.35% | 79.90% | 10.41% | 0.69% | 100.00% |
Padua | 0.90% | 4.77% | 0.03% | 2.35% | 4.65% | 85.88% | 1.42% | 100.00% |
Rovigo | 2.22% | 0.27% | 0.02% | 0.11% | 2.42% | 6.50% | 88.46% | 100.00% |
SPd | 19.04% | 20.20% | 4.43% | 19.54% | 13.06% | 19.36% | 4.37% | 100.00% |
Province of Origin | Parameter | Province of Destination | ||||||
---|---|---|---|---|---|---|---|---|
Verona | Vicenza | Belluno | Treviso | Venice | Padua | Rovigo | ||
Verona | No. of O-D pairs | 0.02 | 0.11 | 0.00 | 0.26 | 0.34 | 0.09 | 0.21 |
Average O-D trip value | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.03 | 0.02 | |
Vicenza | No. of O-D pairs | 0.13 | 0.02 | 0.45 | 0.07 | 0.23 | 0.03 | 0.30 |
Average O-D trip value | 0.02 | 0.03 | 0.06 | 0.04 | 0.02 | 0.04 | 0.00 | |
Belluno | No. of O-D pairs | 0.71 | 0.10 | 0.05 | 0.06 | 0.24 | 0.18 | n.a. |
Average O-D trip value | 0.00 | 0.00 | 0.03 | 0.03 | 0.00 | 0.00 | n.a. | |
Treviso | No. of O-D pairs | 0.39 | 0.07 | 0.02 | 0.02 | 0.06 | 0.03 | 0.57 |
Average O-D trip value | 0.00 | 0.04 | 0.04 | 0.02 | 0.01 | 0.05 | 0.00 | |
Venice | No. of O-D pairs | 0.31 | 0.24 | 0.32 | 0.05 | 0.02 | 0.04 | 0.16 |
Average O-D trip value | 0.00 | 0.02 | 0.00 | 0.02 | 0.03 | 0.02 | 0.07 | |
Padua | No. of O-D pairs | 0.12 | 0.04 | 0.28 | 0.05 | 0.05 | 0.02 | 0.08 |
Average O-D trip value | 0.03 | 0.04 | 0.04 | 0.03 | 0.02 | 0.01 | 0.03 | |
Rovigo | No. of O-D pairs | 0.13 | 0.27 | n.a. | 1.41 | 0.16 | 0.10 | 0.02 |
Average O-D trip value | 0.10 | 0.00 | n.a. | 0.00 | 0.14 | 0.02 | 0.05 |
Sampling/Surveyed Days | Number of Trips Made by One Vehicle | Number of Estimated Vehicles | |||
---|---|---|---|---|---|
Mean | Standard Deviation | Min | Max | ||
15.10.2018 | 3.98 | 1.51 | 1 | 19 | 16,760 |
22.10.2018 | 4.02 | 1.52 | 1 | 16 | 16,674 |
07.11.2018 | 4.10 | 1.46 | 1 | 17 | 16,958 |
15.11.2018 | 4.16 | 1.47 | 1 | 16 | 17,139 |
23.11.2018 | 4.28 | 1.49 | 1 | 16 | 17,427 |
Type of Estimation | MSE | RMSE | MAE |
---|---|---|---|
Through AVM/FCD data | 74 | 5507 | 3104 |
Updating using traffic counts | 74 | 5489 | 3040 |
Province of Vehicle Registration | VT [%] | Mean [%] | Standard Deviation [%] | Confidence Interval | |||||
---|---|---|---|---|---|---|---|---|---|
15.10 | 22.10 | 07.11 | 15.11 | 23.11 | Left Side [%] | Right Side [%] | |||
Belluno | 48.91 | 48.04 | 48.91 | 50.22 | 48.04 | 48.82 | 0.80 | 48.12 | 49.52 |
Padua | 58.92 | 58.80 | 59.64 | 60.58 | 58.80 | 59.35 | 0.69 | 58.75 | 59.95 |
Rovigo | 56.19 | 56.19 | 56.67 | 55.95 | 56.19 | 56.24 | 0.24 | 56.03 | 56.45 |
Treviso | 64.00 | 63.78 | 64.75 | 65.23 | 63.78 | 64.31 | 0.58 | 63.80 | 64.82 |
Venice | 46.4 | 46.24 | 46.21 | 47.83 | 46.24 | 46.58 | 0.63 | 46.03 | 47.13 |
Verona | 58.11 | 57.70 | 58.68 | 59.12 | 57.70 | 58.26 | 0.56 | 57.77 | 58.75 |
Vicenza | 56.54 | 56.06 | 57.66 | 57.71 | 56.06 | 56.81 | 0.74 | 56.16 | 57.46 |
Province of Origin | Province of Destination | Required No. of Survey Days | ||||||
---|---|---|---|---|---|---|---|---|
Verona | Vicenza | Belluno | Treviso | Venice | Padua | Rovigo | ||
Verona | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Vicenza | 1 | 1 | 5 | 2 | 1 | 2 | 1 | 5 |
Belluno | 1 | 1 | 1 | 1 | 1 | 1 | n.a. | 1 |
Treviso | 1 | 3 | 3 | 1 | 1 | 4 | 1 | 4 |
Venice | 1 | 1 | 1 | 1 | 1 | 1 | 8 | 8 |
Padua | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 3 |
Rovigo | 14 | 1 | n.a. | 1 | 31 | 1 | 5 | 31 |
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Comi, A.; Rossolov, A.; Polimeni, A.; Nuzzolo, A. Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence. Information 2021, 12, 493. https://doi.org/10.3390/info12120493
Comi A, Rossolov A, Polimeni A, Nuzzolo A. Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence. Information. 2021; 12(12):493. https://doi.org/10.3390/info12120493
Chicago/Turabian StyleComi, Antonio, Alexander Rossolov, Antonio Polimeni, and Agostino Nuzzolo. 2021. "Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence" Information 12, no. 12: 493. https://doi.org/10.3390/info12120493