# Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Dataset Description

#### 2.1. Study Area

#### 2.2. Bicycle Network

## 3. Bicycle Flow Analysis

#### 3.1. Cyclists’ Flows from Traditional Counting Methods

^{2}= 0.96). In the city of Bologna, people use bicycles more often than in the past. Surely, such an increase in cycling is determined, like in other cities, by an integrated package of many different and complementary measures, including infrastructure provision, pro-bicycle programs, supportive land-use planning and restrictions of car use [2]. However, today’s bicycle network of Bologna connects the most popular origins and destinations, and the expansion of the cycling network has resulted in an increased level of safety as demonstrated by accident statistics [25]. The increasing bicycle use is also related to an increasing bicycle use of females, growing from a share of below 30% in 2009 to a share of 44% in 2018 [30].

^{2}= 0.81), based on approximately 9000 questionnaires carried out in 14 cities in Central Europe.

#### 3.2. Map Matched Cyclists’ Volumes

#### 3.3. Estimated Cyclists’ Volumes

## 4. Deviation Analysis

- For each matched route $R{}_{j}$ of the set of all matched routes J, determine the shortest route $S{}_{j}$ connecting the first and last link of each matched route.
- For each matched route $R{}_{j}$, identify all $K{}_{j}$ non-overlapping sections where links deviate from the shortest route. Set $DR{}_{jk}$ contains all chosen links of the partial deviation k of route j and set $SR{}_{jk}$ contains all links on the shortest route of deviation k and route j, as illustrated in Figure 9.
- For each of these non-overlapping sections, calculate the partial deviation ${d}_{jk}$ which is the difference between the length of the part of the chosen route segment $DR{}_{jk}$ and the length of the corresponding part of the shortest route segment $SR{}_{jk}$; finally the deviation metrix $DM{}_{i}$ of link i is the sum of partial deviations of all routes that contain link i on one of the shortest route segments. Analytically, the total deviation metric $DM{}_{i}$ of a road link i is the sum of all partial deviations received from all non-overlapping sections of all matched trips and can be expressed as:$$DM{}_{i}={\displaystyle \sum}_{j\in J}{{\displaystyle \sum}}_{k=1}^{{K}_{j}}{\delta}_{ijk}\xb7{d}_{jk}$$

_{4}+ L

_{5}+ L

_{6}− (L

_{1}+ L

_{2}+ L

_{3}). The total deviation metric for the central part of Bologna network is shown in Figure 10.

^{2}= 0.160. The small parameter values result in Odds ratios close to one, which is reasonable considering that attribute values are in the order of 10

^{−2}–10

^{−3}. Other attributes like the node density or the share of mixed bikeway access have turned out not to be significant when included in this model. The signs of the model parameters are reasonable—see also the discussion in Section 6. The calibration has been repeated with GPS traces in Bologna from the ECC of the year 2015. The result of this calibration shows parameter values within the standard error bounds of the result from ECC of the year 2016 shown in Table 2.

## 5. Discussion

^{2}= 0.73) is significantly better than the results obtained by previous studies, e.g., Jestico et al. [22] obtained an R

^{2}of 0.4 for the a.m. peak period. The reason for this difference is likely due to the more detailed network model of Bologna, representing better the cyclists’ freedom to move on all possible links in both directions. Based on this correlation, one crowdsourced cyclist corresponds in average to 59 cyclists counted with traditional methods, which is consistent with previous findings in [22].

## 6. Conclusions

^{2}value of 0.73. This correlation is significantly higher than the results obtained by other studies, most likely due to the more detailed representation of the Bologna network, including footpaths in parks and the possibility to cycle one-way roads in both directions. Due to this high correlation, it has been possible to estimate the absolute bicycle flows on all network links by an appropriate scaling of the map-matched flows. The cyclists’ routes are of great value for the planning of cycling infrastructure and the drafting of cycling policies. The proposed method, which combines bicycle counts at a few main road sections with areas covering GPS traces, can readily be applied in other cities in order to reliably estimate the absolute bike flows of an entire urban area.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**The bike-network map of Bologna [24] and study area (dashed lines).

**Figure 6.**Regression function between manually counted cyclists’ volumes and map matched cyclists’ volumes (May 2016).

**Figure 7.**Estimated unidirectional bicycle flows in cyclists per hour during workday morning peak hours (from 8:30 to 10:30). Flows only on network links where GPS points have been detected.

**Figure 9.**Illustration of the calculation of the total deviation metric for the non-overlapping route section between nodes A and B.

**Table 1.**Road link attributes of chosen and shortest routes of non-overlapping sections and on overlapping sections.

Non-overlapping Sections | Overlapping Sections | |||
---|---|---|---|---|

Shortest route | Chosen route | Chosen vs Shortest | Chosen and shortest route | |

Total length [km] | 8265 | 9975 | +20.7% | 7130 |

Mixed road access share | 32.6% | 25.7% | −21.2% | 28.4% |

Low priority road share | 50.0% | 74.1% | +48.2% | 40.2% |

Reserved bikeway share | 16.4% | 39.2% | +139.0% | 23.5% |

Intersection density [1/km] | 18.5 | 15.9 | −14.2% | 16.1 |

Attribute | Parameters${\mathit{\beta}}_{\mathbf{j}}$ | Odds Ratio | Std. err | z | p > |z| |
---|---|---|---|---|---|

Distance ${D}_{i}$ | −0.0007 | 0.9992 | 9.10 10^{−5} | −7.4585 | 8.7531 10^{−14} |

Reserved bikeway share ${B}_{i}$ | 0.0228 | 1.0230 | 8.82 10^{−4} | 23.1713 | 8.8673 10^{−119} |

Low priority road share $L{P}_{i}$ | −0.0085 | 0.9915 | 6.88 10^{−4} | −12.0599 | 1.7192 10^{−33} |

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**MDPI and ACS Style**

Rupi, F.; Poliziani, C.; Schweizer, J.
Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone. *ISPRS Int. J. Geo-Inf.* **2019**, *8*, 322.
https://doi.org/10.3390/ijgi8080322

**AMA Style**

Rupi F, Poliziani C, Schweizer J.
Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone. *ISPRS International Journal of Geo-Information*. 2019; 8(8):322.
https://doi.org/10.3390/ijgi8080322

**Chicago/Turabian Style**

Rupi, Federico, Cristian Poliziani, and Joerg Schweizer.
2019. "Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone" *ISPRS International Journal of Geo-Information* 8, no. 8: 322.
https://doi.org/10.3390/ijgi8080322