# An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Methodology for Representing Urban Traffic States

## 4. Travel Time Estimation Methodology

#### 4.1. Feature Engineering

- ${S}_{5}$: 5% random data sample from e.g., CAVs;
- $\overline{h}$: Average headway (s) when a traffic light is green;
- ${q}_{\mathrm{I}}$: Progressed flow at an intersection (veh/h);
- $\overline{o}$: Average occupancy of LDs (%);
- $\overline{{p}_{\mathrm{c}}}$: Average red/green phase count (-).

#### 4.2. Mlr Model Definition

#### 4.3. Baseline Model Specification

#### 4.4. Final Model Specification

#### 4.5. Performance Metrics

## 5. Description of Experimental Campaign and Data Sources

#### 5.1. Experimental Campaign with Video Cameras

#### 5.2. Data Sources for Sensor Assessment

## 6. Results

#### 6.1. Traffic State Representation and Sensor-Based Assessment

#### 6.2. Travel Time Estimation Assessment

## 7. Discussion

#### 7.1. Traffic States–Traffic Flow

#### 7.2. Traffic States–Travel Times

#### 7.3. Travel Time Estimation Models

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Defined quantities for derivation of traffic flow and travel time: (

**a**) measurement spots to derive traffic flow ${q}_{s}\left(t\right)$ for all $s=\{1,2,3,4,5,6\}$, (

**b**) pre-defined routes $r=\{1,2,3,4,5,6\}$ to derive all travel times ${\tau}_{r}\left(t\right)$.

**Figure 2.**Test area (in red), Zurich Switzerland. The highlighted network in gray indicates the road segments where data is collected and processed. The traffic light symbols indicate the five implemented traffic control systems.

**Figure 3.**Set-up for empirical measurements: (

**a**) placement and distance from the intersection center (in meters) of six cameras capturing traversing traffic (C01–C06), (

**b**) used camera set-up with HD camera and tripod.

**Figure 4.**Thermal camera set-up: (

**a**) four mounted thermal cameras and distances from the intersection center (in meters) capturing traversing traffic (T1–T4), (

**b**) example of an overhead mounted thermal camera.

**Figure 5.**Flow evaluation of (

**a**) ${q}_{1}\left(t\right)$ and (

**b**) ${q}_{2}\left(t\right)$ at WB from the ground-truth measurement, the ALPR, and the thermal camera T2; (

**c**) shows the matching rate of the ALPR algorithm for both derived flows.

**Figure 6.**Flow evaluation (

**a**) ${q}_{3}\left(t\right)$ and (

**b**) ${q}_{4}\left(t\right)$ at EB from the ground-truth measurement, the ALPR, and the thermal camera T3; (

**c**) shows the matching rate of the ALPR algorithm for both derived flows.

**Figure 7.**Flow evaluation (

**a**) ${q}_{5}\left(t\right)$ and (

**b**) ${q}_{6}\left(t\right)$ at NB from the ground-truth measurement, the ALPR, and the thermal camera T4 and T1; (

**c**) shows the matching rate of the ALPR algorithm for both derived flows.

**Figure 8.**Travel time evaluation for (

**a**) ${\tau}_{1}\left(t\right)$, (

**b**) ${\tau}_{3}\left(t\right)$, (

**c**) ${\tau}_{4}\left(t\right)$ and (

**d**) ${\tau}_{5}\left(t\right)$ with the ground-truth measurement, the ALPR, the set of thermal cameras, and the Google Distance Matrix API data set.

**Figure 9.**10 min WMA travel times estimates of the baseline model, estimation model, compared to the 5% data sample and the ground-truth on ${r}_{3}$.

**Table 1.**Correlation coefficient $\rho $ and MAPE of ALPR (abbreviation LP), thermal camera data (abbreviation TC) and the 10 min MA Ground Truth flow data.

${\mathit{q}}_{1}\left(\mathit{t}\right)$ | ${\mathit{q}}_{2}\left(\mathit{t}\right)$ | ${\mathit{q}}_{3}\left(\mathit{t}\right)$ | ${\mathit{q}}_{4}\left(\mathit{t}\right)$ | ${\mathit{q}}_{5}\left(\mathit{t}\right)$ | ${\mathit{q}}_{6}\left(\mathit{t}\right)$ | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

LP | TC | LP | TC | LP | TC | LP | TC | LP | TC | LP | TC | |

$\rho $ [-] | 0.81 | 0.91 | 0.61 | 0.93 | 0.58 | 0.94 | 0.73 | 0.00 | 0.58 | 0.94 | 0.76 | 0.99 |

$\mathrm{MAPE}$ [%] | 15.54 | 4.83 | 12.64 | 3.33 | 17.20 | 3.86 | 9.17 | 92.45 | 17.20 | 3.86 | 24.42 | 1.22 |

**Table 2.**Correlation coefficient $\rho $ and MAPE of ALPR (abbreviation LP), thermal camera data (abbreviation TC), Google Distance Matrix API data (abbreviation G) and the 10 min MA ground truth flow data. Note that a correlation denoted as NA=not available means that the time series standard deviation is zero.

${\mathit{\tau}}_{1}\left(\mathit{t}\right)$ | ${\mathit{\tau}}_{3}\left(\mathit{t}\right)$ | ${\mathit{\tau}}_{4}\left(\mathit{t}\right)$ | ${\mathit{\tau}}_{5}\left(\mathit{t}\right)$ | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

LP | TC | G | LP | TC | G | LP | TC | G | LP | TC | G | |

$\rho $ [-] | 0.99 | 0.71 | 0.27 | 0.84 | −0.11 | NA | 0.85 | −0.13 | NA | 0.99 | 0.72 | 0.33 |

$\mathrm{MAPE}$ [%] | 3.14 | 58.07 | 25.95 | 8.04 | 18.36 | 50.64 | 7.51 | 26.76 | 73.38 | 2.73 | 20.60 | 45.49 |

**Table 3.**Adjusted R-square values of the model performance on the training data. MAPE denotes the comparison of travel times estimates (baseline model and estimation model), and 5% sample of ${r}_{3}$.

${\mathit{r}}_{3}$ | $\mathbf{adj}{\mathit{R}}^{2}$ [-] | MAPE [%] |
---|---|---|

5% sample | - | 18.10 |

Base Model | 0.40 | 11.62 |

Final Model | 0.81 | 10.92 |

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**MDPI and ACS Style**

Genser, A.; Hautle, N.; Makridis, M.; Kouvelas, A.
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation. *Sensors* **2022**, *22*, 144.
https://doi.org/10.3390/s22010144

**AMA Style**

Genser A, Hautle N, Makridis M, Kouvelas A.
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation. *Sensors*. 2022; 22(1):144.
https://doi.org/10.3390/s22010144

**Chicago/Turabian Style**

Genser, Alexander, Noel Hautle, Michail Makridis, and Anastasios Kouvelas.
2022. "An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation" *Sensors* 22, no. 1: 144.
https://doi.org/10.3390/s22010144