Correlation between Space Syntax and Vehicle Traffic Volumes
Made in Milan by Transform Transport Š 2018, Systematica Srl All studies presented in this book are developed by Transform Transport. All rights reserved. Unauthorised use is prohibited. No part of this publication may be reproduced in any form or by any means without the written permission of Systematica Srl.
Team: Filippo Bazzoni, Rawad Choubassi, Efrem Errera, Francesco Scarnera A special thanks to all collaborators of Systematica who contributed to this book.
Systematica Srl Transport Planning and Mobility Engineering
Milan Beirut Mumbai
Via Lovanio, 8 20121 - Milan Italy
T + 39 02 62 31 19 1 E milano@systematica.net
www.systematica.net
Table of contents 1. INTRODUCTION Research goal Four step traffic model (CUBE) Space Syntax and spatial analysis
p. 03 p. 04 p. 05 p. 06
2. TRAFFIC MODEL OF MILAN Traffic volume estimate Available data in the model
p. 07 p. 09 p. 11
3. SPATIAL ANALYSIS OF MILAN Different outputs of spatial analysis Network choice analysis Choice analysis at different distances Normalized measure of choice (NACH)
p. 13 p. 15 p. 17 p. 19 p. 21
4. CORRELATION BETWEEN TRAFFIC MODEL AND SPACE SYNTAX Methodology Outputs of NACH*road capacity Correlation between NACH*Capacity and Traffic Volumes (CUBE)
p. 25 p. 27 p. 29 p. 33
5. CONCLUSIONS AND NEXT STEPS
p. 37
6. REFERENCES
p. 39
Traffic models are extensively used to forecast future traffic conditions with different mobility patterns, land uses or network configurations. Their robustness and reliability is well acknowledged and the majority of national, regional and urban mobility plans are done with the support of this tool. However, their implementation requires a rigorous approach that is time consuming and fairly expensive, as it grounds on large amount of data collection and specialist works, so that not every Municipality or stakeholder can make use of this instrument.
Spatial configurations of networks are also being increasingly used to preliminarily assess potential movements of vehicles and pedestrians. This methodology, born approximately thirty years ago in the UK, is arguing that route choice is strongly affected by the configuration of the space and by the way the different parts of complex system are related to each other, according to the same rules that are applied to mathematical graph theories. This research is aimed to test to extent to which this is true for the Metropolitan area of Milan.
CUBE_Traffic Volume
Space Syntax _Choice
Space Syntax_Normalized Choice
Space Syntax_Normalized Choice*Road Capacity
To what extent road network configurational characteristics can anticipate vehicle traffic movements? The aim of this research is to measure the degree of correlation between predicted traffic volumes (with CUBE software by Citilabs) and the configurational values of the same network depicted through spatial network analysis (using DepthMap software by UCL). The research focuses on the City of Milan since the researchers have access to a fully reliable and well calibrated traffic model, as well as an excellent knowledge of the city. There are two main findings: first, traffic volumes have higher correlation with choice values (calculated with DepthMap) once these are normalized. Second, a significant correlation is found between traffic volumes and normalized choice multiplied per road capacity; this value could find useful applications for traffic prediction analysis.
Examples of elaborations using CUBE and Space Syntax data
02
Traffic Volumes_USA
Pedestrian Movement Analysis
Š Citilabs
Traffic Volumes_Torrance
Traffic Model
A transportation model is the instrument to support decisions when planning and designing transportation systems. Macroscopic forecast modelling platforms are the most powerful tool providing advanced methods for transportation modelling: urban, regional and long distance demand forecasting and assignment. These software are commonly used for developing standard four-step multimodal traffic models (trip generation, trip distribution, mode choice, and assignment). Trip generation models are typically based on demographic data; Distribution models predict trips from homes to destinations such as work places, retail facilities, and other homes; Mode choice models predict the split of travel choices (private car, public transport, etc.); Assignment models predict the actual route taken, given an origin, a destination, and a mode of transport.
Example of four-step model representing traffic volume and pedestrian movements 04
Agent analysis
Segment analysis
Š UCL DepthMap 10
VGA analysis
Axial analysis
Convex analysis_Connectivity
Space Syntax Spatial Analysis
Space syntax investigates relationships between spatial layout and a range of social, economic and environmental phenomena. These phenomena include patterns of movement, awareness and interaction; density, land use and land value; urban growth and societal differentiation; safety and crime distribution (SSN, 2018). From a transport perspective, the most innovative factor of this discipline consists in describing convenient movements not just in terms of time or distance, but also the angular deviation from a straight route from origin and destination. The shortest path is therefore determined not only by metric distance, but also by relative cost of turning, whereby greater turns are costlier: this is confirmed by more than three decades of observations and studies, still ongoing, that generally people tend to move preferring intuitiveness to distance, especially on foot.
Example of Space Syntax analysis (Agent, VGA, Convex, Segment, Axial)
06
CHAPTER 2
Traffic Model Of Milan
Space Syntax and Traffic Volumes
November 2018
Traffic Volume Prediction
The image on the right shows the traffic model of Milan metropolitan area used within this research. This model is a reliable tool that is calibrated to replicate existing traffic conditions with great accuracy. The roads with higher traffic flows are shown in red, primarily the radial and ring road highway system, while in yellow the local roads with lower traffic loads.
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Traffic volumes Highest Lowest
Data available in the Model
Traffic Volume
Within the model, a variety of data are associated to each road such as width, speed, travel direction, type of road, etc. In particular, within this study the following is considered: Traffic Volumes which represent the amount of vehicles passing through different roads; Road Capacity which represents the maximum vehicle capacity for each road; Road Speed which represents the maximum posted speed limit of different roads.
Traffic volumes 8496 (Highest) 0 (Lowest)
11
Road Capacity
Road Capacity
Speed
Speed
10000 (Highest)
130 (Highest)
0 (Lowest)
0 (Lowest)
CHAPTER 3
Spatial Analysis Of Milan
Different Outputs of Spatial Analysis
Street Network Choice
Within Space Syntax discipline, there is a wide range of configurational parameters that can describe each link of a network. The images on the right show the three main parameters that are currently being used in the majority of the studies. In particular: Choice measures movement flows through spaces. Spaces that record high global choice are located on the shortest paths from all origins to all destinations. Choice is a powerful measure for forecasting forecasting potential pedestrian and vehicle movements. Integration is a measure of mean depth that is specifically adapted for architectural layouts. The global measure shows how deep or shallow a space is in relation to all other spaces. Using integration, spaces are ranked from the most integrated to the most segregated. Integration is usually indicative to how many people are likely to be in a space, and is thought to correspond to rates of social encounter and retail activities (Hillier, 1996a). Connectivity (degree) measures the number of immediate neighbors directly connected to a space or road.
Street Network Choice Most chosen path Least chosen path
15
Road Capacity
Integration
Connectivity
Connectivity
Highest
Highest
Lowest
Lowest
Network Choice Analysis
Network Choice depicts the degree to which the streets form a part of the shortest trip from all origins to all destinations. movement: these streets are inherently important parts of many different journeys. Choice is computed as follows:
Angular Choice reveals the streets that are inherently on many shortest paths, when considering all origins and all destinations within a certain network distance radius. The shortest path is determined not by metric distance, but by relative cost of turning, whereby greater turns are more costly. The spatial network analysis at a distance=n (taking all segments of the network as both possible origins and destinations) highlights the most chosen paths inside the network in red and the least chosen paths in the network in blue. Higher choice value
17
Lower choice value
Street Network Choice Most chosen path Least chosen path
Choice Analysis at Different Distances
Choice 500meters
By changing the scale for choice analysis, results are different depending on the applied distance, describing different configurations that are ideal for different travel modes. Choice distance = 500meters Analysis at very small scales highlight those streets, and particularly junctions, that are ideal for community services, small local businesses that require high pedestrian footfall.
Choice 2500meters
Choice distance = 1000meters This is the ideal distance everyone is willing to walk on foot, and where walking is much more convenient than all other modes; the potential areas for being center of neighborhood activities are identified. Choice distance = 2500 meters Increasing the distance of the analysis is possible to identify the most convenient routes for bicycles and other small electric vehicles, understanding strategic corridors to connect adjacent districts in a sustainable manner. Choice distance = n meters (infinite distance) possible to preliminarily identify arteries convenient for long distance movements, therefore most likely by car or by bus at urban scale. Street network Choice Most chosen path
Least chosen path
19
Choice 1000meters
Choice n meters
Normalized Measure of Choice (NACH)
Correlation (r2) between Choice and Traffic Volume = 0.0990 (at distance=n) Choice TRAFFIC VOLUME (CUBE) 140% 120% 100% 80% 60%
R² = 0,099
40% 20% 0% 0
Normalized choice aims to solve the paradox that segregated designs add more total (and average) choice to the system than integrated ones. It divides total choice by total depth for each segment in the system. This adjusts choice values according to the depth of each segment in the system, since the more segregated it is, the more its choice value will be reduced by being divided by a higher total depth number. This would seem to have the effect of measuring choice in a cost-benefit way (Hillier et al., 2012).
2000
4000
6000
8000
10000
Correlation (r2) between NACH and Traffic Volume = 0.1727 (at distance=n) NACH - TRAFFIC VOLUME (CUBE) 140%
R² = 0,1727
120%
Normalized Angular Choice NACH is defined as follows;
100% 80% 60% 40% 20% 0% 0
2000
4000
6000
8000
where (i, x, j)=1 if the shortest path from i to j passes through x and 0 otherwise.
Finding #1: NACH as higher correlation with Traffic Volumes
21
10000
Space Syntax model representing NACH values at distance=n
NACH Highest Lowest
Graphical representation of standard values of choice is significantly different to values generated by normalized measure of choice (NACH). The latter representation is, in fact, more coherent with real traffic distribution.
Choice_n
Street Network Choice Most chosen path Least chosen path
Space Syntax and Traffic Volumes
November 2018
NACH_n
Street Network NACH Highest Lowest
CHAPTER 3
Correlation between Traffic Model and Space Syntax
Space Syntax and Traffic Volumes
November 2018
Methodology Extracting CUBE Predicted Traffic Volumes
Calculating Space Syntax Choice Values at different Distances The methodology applied to test correlation between forecasted traffic volumes and Space Syntax values consists in five different steps: First step. Extracting traffic volumes from the macroscopic traffic model and calculating choice values in Space Syntax at different distances; Second step. Adding variables to choice values (Road Capacity, Speed and Segment Length) obtained from the macroscopic traffic model; Third step. Calculating normalized measures of choice (NACH) and adding variables; Fourth step. Calculating correlation between values; Fifth step. Analyzing results through chart/data and graphical comparison.
(500, 1000, 2500, 5000, 10000, 15000, 20000, n meters)
Calculating Normalized measure of Choice (NACH)
Analysis of Results (Chart/Data and Graphical Comparison)
27
Choice*Road Capacity
Adding Variables to Choice Values
Choice*Speed Choice*Segment_Lenght
Calculating Correlation (r2)
NACH*Road Capacity
Adding Variables to NACH Values
NACH*Speed NACH*Segment_Lenght
METROPOLITAN AREA (Correlation 0<r2<1) Distance r:
Choice Choice x x road speed capacity
Choice x segm. lenght
NACH x Choice road capacity
NACH x speed
NACH x segm. lenght
NACH
500
0.0126
0.0223
0.0149
0.0291
0.1948
0.0402
0.0016
0.0181
1000
0.0298
0.0510
0.0354
0.0671
0.4388
0.2183
0.0092
0.001
2500
0.0583
0.0930
0.0758
0.1168
0.6315
0.4752
0.0402
0.0391
5000
0.0768
0.1157
0.0933
0.1376
0.6898
0.5643
0.0582
0.102
10000
0.0992
0.1494
0.0908
0.1723
0.7086
0.6007
0.0668
0.1479
15000
0.0962
0.1436
0.0835
0.1641
0.7123
0.61
0.0685
0.162
20000
0.0909
0.1347
0.0799
0.1530
0.7129
0.6132
0.0696
0.1668
n
0.0865
0.1276
0.0791
0.1444
0.7105
0.6126
0.0688
0.1727
28
NACH*Road Capacity Outputs NACH*Road Capacity presents the highest correlation values with forecasted traffic volumes
Several tests were made at different calculation distances. Analysis of results is interesting if we look at charts comparison and graphical restitution. The charts on the right represent only correlation values for the NACH*Road Capacity column (which show the highest values). Graphical representation of results is given in the following page.
METROPOLITAN AREA (Correlation - 0<r2<1)
Choice x road capacity
Choice x speed
Choice x segm. lenght
Choice
NACH x road capacity
500
0.0126
0.0223
0.0149
0.0291
0.1948
1000
0.0298
0.0510
0.0354
0.0671
2500
0.0583
0.0930
0.0758
5000
0.0768
0.1157
10000
0.0992
15000
NACH x segm. lenght
NACH
0.0402
0.0016
0.0181
0.4388
0.2183
0.0092
0.001
0.1168
0.6315
0.4752
0.0402
0.0391
0.0933
0.1376
0.6898
0.5643
0.0582
0.102
0.1494
0.0908
0.1723
0.7086
0.6007
0.0668
0.1479
0.0962
0.1436
0.0835
0.1641
0.7123
0.61
0.0685
0.162
20000
0.0909
0.1347
0.0799
0.1530
0.7129
0.6132
0.0696
0.1668
n
0.0865
0.1276
0.0791
0.1444
0.7105
0.6126
0.0688
0.1727
Distance (m):
29
NACH x speed
Distance=500m
Distance=1000m
120%
120%
100%
100%
80%
80%
60%
60%
40%
R² = 0,1948
R² = 0,4388
40% 20%
20%
0%
0% 0
2000
4000
6000
8000
0
10000
2000
4000
6000
8000
10000
Predicted Traffic Volume
Predicted Traffic Volume
Distance=2500
Distance=5000m
120%
120%
100%
100%
80%
80%
60%
R² = 0,6315
40%
40%
20%
20%
0%
R² = 0,6898
60%
0% 0
2000
4000
6000
8000
10000
0
2000
Predicted Traffic Volume
4000
6000
8000
10000
Predicted Traffic Volume
Distance=10000m
Distance=15000m
120%
120%
100%
100%
80%
80% R² = 0,7086
60% 40%
40%
20%
20%
0%
R² = 0,7123
60%
0% 0
2000
4000
6000
8000
10000
0
2000
Predicted Traffic Volume
4000
6000
8000
10000
Predicted Traffic Volume
Distance=20000m
Distance=n
120%
120%
100%
100%
80%
80% R² = 0,7129
60% 40%
40%
20%
20%
0%
R² = 0,7105
60%
0% 0
2000
4000
6000
Predicted Traffic Volume
8000
10000
0
2000
4000
6000
Predicted Traffic Volume
8000
10000
Distance=500m
Distance=1000m
Distance=10000m
Distance=15000m
NACH*Road Capacity Highest Lowest
Space Syntax and Traffic Volumes
November 2018
Distance=2500m
Distance=5000m
Distance=20000m
Distance=n
GIS elaborations representing NACH*Road Capacity values at different distances
Correlation between NACH*Capacity and Traffic Volumes The correlation between NACH*Capacity values and Traffic Volumes reach results that start from a minimum correlation value (r2 ) of 0.1948 (in case of distance=500m) to a maximum of 0.7129 (in case of distance=20000m).
NACH*CAPACITY (distance=20000m) 120% 100%
Generally, correlations higher than 0.7 are considered statistically significant. As shown in the bottom right chart, this value is reached for values of Capacity*NACH at all distances above 2500 meters, which is in line with travel done by private vehicles.
80% R² = 0,7129 60% 40%
The distance of 20 Km is the one that shows most successful results and it is graphically represented in the image on the right.
20% 0% 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Predicted Traffic Volume
METROPOLITAN AREA (Correlation - 0<r2<1)
33
NACH x speed
Distance r:
NACH x road capacity
500
0.1948
1000
Metropolitan Area (0 < r2 < 1)
NACH x segm. lenght
NACH
0.0402
0.0016
0.0181
0,6
0.4388
0.2183
0.0092
0.001
0,5
2500
0.6315
0.4752
0.0402
0.0391
0,4
5000
0.6898
0.5643
0.0582
0.102
0,3
10000
0.7086
0.6007
0.0668
0.1479
0,2
15000
0.7123
0.61
0.0685
0.162
0,1
20000
0.7129
0.6132
0.0696
0.1668
n
0.7105
0.6126
0.0688
0.1727
0,8 0,7
0 500
1000
NACH*CAPACITY
2500
5000
NACH*SPEED
10000
15000
20000
NACH*SEG_LENGHT
n NACH
NACH*Capacity Highest Lowest
GIS elaboration representing NACH*Road Capacity values at distance=20Km
Graphical representation of Four-Step Traffic model (left) is very similar compared to the map representing NACH*Road Capacity values at distance=20000m (right)
Four-Step Traffic model
Traffic volume Highest Lowest
Space Syntax and Traffic Volumes
November 2018
NACH*Road Capacity (distance=20Km)
NACH*Capacity Highest Lowest
Conclusion and next steps The main goal of this research is to measure the correlations between traffic volumes and the space syntax analysis outputs. Main findings are: High correlation levels are recorded between traffic volumes and choice values if normalized (NACH); Higher correlation levels are recorded between traffic volumes and Normalized Angular Choice (NACH) values multiplied by Road Capacities (or other variables like speed). Further research might: Carry additional tests and check correlations for different road categories (highways, collector roads, local roads, etc); Test other case studies and compare results with the case of Milan; Introduce other measures in space syntax in order to directly weigh choice values for different parameters, such as land use, population, etc.
37
References CITILABS, (2018). Retrieved on 08/11/2018 through http://www.citilabs.com/software/cube/cube-voyager/. Hillier, B., (1996a). Urban Design International, 1(1): 41-60. Hillier, B., Yang, T., Turner, A., (2012). Advancing depthmap to advance our understanding of cities. In: Greene, M and Reyes, J and Castro, A, (eds.) 8th International Space Syntax Symposium. Pontificia Universidad Catolica de Chile: Santiago, Chile. SSN, (2018). Space Syntax Network. Retrieved on 08/11/2018 through http://www.spacesyntax.net/. Systematica, (2018). Reading Dubai Structure. Retrieved on 08/11/2018 through http://research.systematica.net/research/reading-dubaistructure/.
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