Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data
Abstract
:1. Introduction
2. Background
2.1. Sixth Edition Highway Capacity Manual BLOS (HCM6)
2.2. Bicycle Compatibility Index (BCI)
- BL = presence of a bicycle lane or paved shoulder > 3.0 ft no = 0 yes = 1
- BLW = bicycle lane width in feet (to the nearest tenth)
- CLW = curb lane width in feet (to the nearest tenth)
- CLV = curb lane vehicles per hour in the travel direction
- OLV = other lane(s) volume in travel direction
- SPD = 85th percentile vehicle speeds miles/h
- PKG = presence of a parking lane with more than 30 percent occupancy; no = 0, yes = 1
- AREA = type of roadside development; residential = 1 other type = 0
- AF = adjustment factor for truck volumes, parking turnover and right-turn volumes
2.3. Level of Traffic Stress (LTS)
2.4. Bicycle Stress Level (BSL)
3. Methods
3.1. Survey and Mapping API
3.2. Network Information
3.3. Data Preparation
3.4. Network Impedance Based on BLOS
3.5. Detour Rate
3.6. Route Choice Generation and Evaluation with Empirical Data
- Collect necessary transport and land use GIS parameters in the area of interest from existing data sources (see Table 2) or field data.
- Combine the necessary parameters to produce the BLOS index value for each link in the transport network area using GIS attribute tables.
- Create a range of plausible detour rates and corresponding impedance factors (for different BLOS levels) from the shortest path (e.g., 0 to 50% in this paper). See example in Table 4 for LTS.
- Create a new parameter for each link ‘perceived link length’ by multiplying the link length with the impedance factors from step 3.
- Create a new parameter ‘perceived intersection length’ for intersections with three or more links and variability in BLOS amongst links (see lookup example in Table 5 for LTS).
- Combine the two components for each link to produce a new parameter ‘perceived length’. This is the sum of ‘perceived link length’ and the relevant ‘perceived intersection length’ lookup value for cases in which the link intersects another link with a lower (poorer standard) BLOS.
- Calculate a new parameter ‘perceived travel time’ using ‘perceived length’ and the underlying topography (in this study, performed using Network Analyst in ArcGIS). For this paper, travel time is dependent on cycling speed which is a direct function of link gradient the Norwegian Area and Transport Planning (ATP) model.The ATP model is an ArcGIS extension which performs a variety of functions and includes a simple speed model for different gradients. On slopes with a gradient of −10% or more (downhill), a maximum speed of 40 kph is used. Similarly, above 8% gradient (uphill), a constant speed of 3 kph is used. On level ground, cyclists are assumed to cycle at 16 kph. Speed is linearly decreased as the gradient increases from 0 to 8% and is linearly increased when the (downhill) gradient approaches −10% (from 0% gradient). Note that the original link gradient is assumed to apply to the ‘perceived length’.
- Now, for each OD pair and detour combination, find the optimal route which minimises the perceived travel time (these are hereafter called generated routes). Since there are 11 different detour rates iterated in this example, each OD pair will have 11 (not necessarily unique) generated routes.
- For each OD pair, find the degree of overlap between the empirical map-matched routes and the generated routes. Since there are very few empirical routes that use the entirety of the generated route, we can measure instead the number of cyclists on each link of the shortest path to give a ‘length-weighted’ number of cyclists on a generated route according to the numbers of cyclists found to use its component links. This is described using the notation in step 10.
- Say that there are n unique generated routes of interest R1,…,Rn. For each j ∈ {1,…,n} we have mj links, and the lengths of these links are denoted by L1,j, L2,j,…,. The total length of the jth route would then be . Now let Ci be the number of cyclists recorded on link i. Then the number of ‘weighted cyclists’ (denoted by Wi) on link i within route j is therefore .The percentage of cyclists on a specific generated route is then the sum of weighted cyclists along that route’s component links divided by the total number of participant-drawn routes (which we know from Section 3.3 to be 467) as given by Equation (3)
- Plot the percentage cyclists on each generated route Rj against the iterated detour rates (on the x-axis. The optimal value (highest percentage match on the y-axis) provides an empirical indicator of willingness to deviate from the shortest path to use high-quality infrastructure (in terms of bicycle suitability in relation to surrounding options).
4. Results
4.1. BLOS Map Creation and Empirical Route Choices
4.2. Route Generation
4.3. BLOS Model Comparison
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Method Name | Bicycle Safety Index Rating | Bicycle Stress Level | Florida Roadway Condition Index | Bicycle Interaction Hazard Score | Bicycle Suitability Rating | Real-Time Bicycle LOS | Bicycle Compatibility Index | Danish Bicycle LOS | 6th ed. US Highway Capacity Manual | Level of Traffic Stress | Bicycle LOS—India | Evaluation of Bicycle Suitability |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Reference | Davis, 1987 [33] | Sorton & Walsh, 1994 [34] | Epperson, 1994 [33] | Landis, 1994 [35] | Davis, 1995 (Davis, 1995 in [36]) | Landis et al., 1997 [10] | Harkey et al., 1998 [37] | Jensen, 2007 [38] | TRB, 2016 [8,39] | Mekuria et al., 2012 [31,40] | Beura et al., 2018 [41] | Majumdar& Mitra, 2018 [32] |
Acronym | BSIR | BSL | RCI | IHS | BSR | RTBLOS | BCI | DBLOS | HCM6 | LTS | BLOS-I | EBS |
AADT | - | - | - | - | - | - | - | - | - | - | - | - |
Bicycle Facility Width/Presence | P | P | P | P | ||||||||
Bicycle Separation from Traffic | P | |||||||||||
Bus Facilities | - | - | ||||||||||
Driveways | - | - | - | - | - | - | - | |||||
Kerb Height/Presence | - | - | - | |||||||||
Land Use Intensity | - | - | - | - | - | - | X | - | - | |||
Lighting | ||||||||||||
Line of Sight | P | P | P | |||||||||
Median Strip | P | P | P | |||||||||
Number/Type of Traffic Lanes | P | P | P | P | P | P | P | P | - | |||
On-street Parking | - | - | - | - | - | - | - | - | - | - | ||
Percentage of Heavy Vehicles | - | - | - | |||||||||
Road Grade | - | - | - | |||||||||
Shoulder | P | - | P | P | P | P | ||||||
Signage-Bicycles | ||||||||||||
Signalised Intersection | - | |||||||||||
Speed | - | - | - | - | - | - | - | - | - | - | - | P |
Surface Quality | P | P | P | P | P | P | P | P | ||||
Traffic Calming Features | ||||||||||||
Turning/Crossing Traffic | - | - | ||||||||||
Vegetation/ Green Space | ||||||||||||
Width of outside Lane (inc. Bike lane/shoulder) | P | P | P | P | P | P | P | P | P | P | P |
Data Source | Input Data Set | Data Type |
---|---|---|
Norwegian Mapping Authority & Norwegian Public Roads Administration | Street network including paths and topography (Norwegian “elveg” database) | Geodatabase—centrelines of roads |
Norwegian National Road Database | AADT traffic volumes, speed limit and lane width data | ArcMAP API toolbox |
Authors, kart.finn.no aerial photography | Missing links for pedestrians and bicycle users. Supplementary information for the network (parking, bicycle lanes, kerb presence) | Geodatabase (manual editing) |
Survey respondents | Mapped bicycle route choice (mapping API) | Geographic JavaScript Object Notation (GeoJSON) |
LTS Level | Impedance Factor for Links (for Max Detour Rate of 15%) | Virtual Buffer Length (in Metres) | Maximum Penalty Length (in Metres) |
---|---|---|---|
1 (best) | 1 | 0 | 0 |
2 | 1.05 | 8.33 | 0.42 |
3 | 1.10 | 16.67 | 1.67 |
4 (worst) | 1.15 | 25 | 3.75 |
Detour Rate (Percentage Additional Length) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
LTS level | 0 | 5 | 10 | 15 | 20 | 25 | 30 | 35 | 40 | 45 | 50 |
1 (best) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
2 | 1 | 1.02 | 1.03 | 1.05 | 1.07 | 1.08 | 1.10 | 1.12 | 1.13 | 1.15 | 1.17 |
3 | 1 | 1.03 | 1.07 | 1.10 | 1.13 | 1.17 | 1.20 | 1.23 | 1.27 | 1.30 | 1.33 |
4 (worst) | 1 | 1.05 | 1.10 | 1.15 | 1.20 | 1.25 | 1.30 | 1.35 | 1.40 | 1.45 | 1.50 |
Detour Rate (Percentage Additional Length) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
LTSlink to LTSmax | 0 | 5 | 10 | 15 * | 20 | 25 | 30 | 35 | 40 | 45 | 50 |
1 to 2 | 0 | 0.14 | 0.28 | 0.42 * | 0.56 | 0.69 | 0.83 | 0.97 | 1.11 | 1.25 | 1.39 |
1 to 3 | 0 | 0.56 | 1.11 | 1.67 * | 2.22 | 2.78 | 3.33 | 3.89 | 4.44 | 5.00 | 5.56 |
1 to 4 | 0 | 1.25 | 2.50 | 3.75 * | 5.00 | 6.25 | 7.50 | 8.75 | 10.00 | 11.25 | 12.50 |
2 to 3 | 0 | 0.42 | 0.83 | 1.25 | 1.67 | 2.08 | 2.50 | 2.92 | 3.33 | 3.75 | 4.17 |
2 to 4 | 0 | 1.11 | 2.22 | 3.33 | 4.44 | 5.56 | 6.67 | 7.78 | 8.89 | 10.00 | 11.11 |
3 to 4 | 0 | 0.69 | 1.39 | 2.08 | 2.78 | 3.47 | 4.17 | 4.86 | 5.56 | 6.25 | 6.94 |
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Pritchard, R.; Frøyen, Y.; Snizek, B. Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data. ISPRS Int. J. Geo-Inf. 2019, 8, 214. https://doi.org/10.3390/ijgi8050214
Pritchard R, Frøyen Y, Snizek B. Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data. ISPRS International Journal of Geo-Information. 2019; 8(5):214. https://doi.org/10.3390/ijgi8050214
Chicago/Turabian StylePritchard, Ray, Yngve Frøyen, and Bernhard Snizek. 2019. "Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data" ISPRS International Journal of Geo-Information 8, no. 5: 214. https://doi.org/10.3390/ijgi8050214
APA StylePritchard, R., Frøyen, Y., & Snizek, B. (2019). Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data. ISPRS International Journal of Geo-Information, 8(5), 214. https://doi.org/10.3390/ijgi8050214