# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data

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## Abstract

**:**

## 1. Introduction

#### 1.1. Background and Objectives of Study

#### 1.2. Content and Method of Study

## 2. Theory and Prior Studies

#### 2.1. Speed Limit Setting Criteria

#### 2.1.1. Korean Criteria

#### 2.1.2. Overseas Criteria

#### 2.2. Speed Limit Estimation Method

#### 2.2.1. Engineering Study Methods

#### 2.2.2. Optimal Speed Limits

#### 2.2.3. Expert System Approach

#### 2.2.4. Injury Minimization Approach

#### 2.3. Speed and Traffic Safety

#### 2.4. Motivation and Differentiation of Research

## 3. Data Collection and Model Equation Development

#### 3.1. Research Methodology

#### 3.1.1. Basic Concept and Procedure

_{1}× f

_{2}× f

_{3}×, … ×, f

_{i},

_{i}is factor for adjusting the effects of roadside conditions (based on the calculation of the adjustment factor).

- The sections must have a low accident rate;
- The sections must have a low standard deviation with respect to traffic flow.

#### 3.1.2. Development of Adjustment Factor Estimation Criteria

_{i}) and its corresponding adjustment factor (f

_{i}) have a linear relationship. Figure 7 shows an abbreviated form of f

_{i}between 0 and 1 on the y-axis. In addition, the x-axis variable was standardized to a value between 0 and 1. Then, if the standardized variable is denoted as SV

_{i}, the adjustment factor can be obtained as follows:

_{i}= 1 − s ν

_{i},

_{i}is standardized variable i.

#### 3.1.3. Variable Standardization

_{ij}= 1 − (ν

_{ij}× α

_{i})/80,

_{ij}is the adjustment factor of variable j at section I; V

_{ij}is the coefficient of variable j at section I; and α

_{i}is the slope from the linear regression estimation equation.

#### 3.1.4. Weighting Factors

_{1j}× α

_{i})/80]

^{w}

_{1}× [1 − (ν

_{2j}× α

_{2})/80]

^{w}

_{2}× [1 − (ν

_{3j}× α

_{3})/80]

^{w}

_{3}×,⋯,× [1 − (ν

_{ij}× α

_{i})/80]

^{w}

_{i},

_{i}is weight for calculating other effects of the variable in the model equation.

#### 3.1.5. Model Test and Utilization

#### 3.2. Data Collection

#### 3.2.1. Road Section Traffic Volumes

^{2}= 0.835), with the model equation demonstrating a high goodness of fit and a reliable coefficient (p < 0.001). The constant and regression coefficient were estimated to be 10,676 and 8.368, respectively.

#### 3.2.2. Selection of Road Sections

#### 3.2.3. Estimation of Accident Rate by Road Section

#### 3.2.4. Selection of Safe Road Sections

#### 3.2.5. Extracting Speed Data

#### 3.2.6. Variables Related to Roadside Speed

#### 3.3. Correlation Analysis and Variable Selection

#### 3.4. Development of Adjustment Factor Estimation Criteria

_{i}) corresponding to a variable (V

_{i}) was assumed to be linear.

#### 3.4.1. The Adjustment Factor (f_{RFC}) Pertaining to the Road Function

_{RFC}) pertaining to the road function is a categorical variable and was classified by road type—(1) arterial roads, (2) auxiliary arterial roads, and (3) collector roads—and estimated to be similar to the binomial optional variable. The estimated slope in the regression equation was −5.11, while the constant was estimated to be 61.10:

_{RFC}.

_{RFCj}= 1.00 − 0.13 × (V

_{RFCj}− 1)/2,

_{RFC}represents arterial, auxiliary arterial, and collector roads accordingly. The estimation process is expressed in a graph, as shown in Figure 13.

#### 3.4.2. The Adjustment Factor (f_{CD}) Pertaining to the Median Strip

_{CD}for the last survey section j can be expressed as follows:

_{CDj}= 0.82 + 0.18 × (V

_{CDj}),

_{CDj}is 1 if there is a median and 0 otherwise.

#### 3.4.3. The Adjustment Factor (f_{PL}) Pertaining to the Level of Parking

_{PL}) expressed as:

_{PLj}= 1.00 − 0.16 × (V

_{PLj}− 1)/2,

_{PL}= 1 if the level of parking is almost zero, 2 if it is low, and 3 if it is high.

#### 3.4.4. The Adjustment Factor (f_{AD}) Pertaining to the Number of Access Density

_{AD}) and the adjustment factor are constant for both the x- and y-axes. The larger the number of access points is, the lower the speed is. The slope estimated by the regression equation (α

_{AD}) was −0.36, corresponding to a constant of 58.63.

_{AD}) for the x-axis is 162.9. If the estimated regression line is projected to be 80 km, the constant (δ

_{AD}) corresponding to the x-axis is. Thus, when the total number of access points per km is approximately, the travel speed, in theory, becomes zero:

_{AD},

_{AS}= 80/α

_{AD}= 80/0.36 = 222.2.

_{ADj}= V

_{ADj}/δ

_{AD}, f

_{ADj}= 1.0 − SV

_{ADj}= 1.0 − (V

_{ADj}/δ

_{AD}),

_{ADj}denotes the standardized number of access points for the jth section, V

_{ADj}denotes the number of access points for the jth section, and δ

_{AD}is the x-axis constant corresponding to the 80 km projected line.

_{ADj}= 1.0 − (V

_{ADj}× α

_{AD})/80 = 1.0 − (V

_{ADj}/222.2).

#### 3.4.5. The Adjustment Factor (f_{SD}) Pertaining to the Number of Traffic Breaks

_{SD}) is obtained via the same process. The larger the number of traffic break facilities is, the lower the travel speed is. The slope estimated by the regression equation is −2.13 and the constant is 62.53. In the following equation, the constant (y

_{SD}) for the x-axis is 29.4. When the estimated regression line is projected to be 80 km, the constant for the x-axis (δ

_{SD}) is 37.6. Therefore, when the number of traffic break facilities per km is approximately, the travel speed theoretically becomes zero:

_{SD},

_{SD}= 80/α

_{SD}= 80/2.13 = 37.6.

_{SDj}= V

_{SDj}/δ

_{SD}, f

_{SDj}= 1.0 − SV

_{SDj}= 1.0 − (V

_{SDj}/δ

_{SD}),

_{SDj}denotes the standardized number of traffic break facilities for the jth section, V

_{SDj}is the number of traffic break facilities surveyed in the jth section, and δ

_{SD}is the constant of the x-axis for the 80 km projected line.

_{SDj}= 1.0 − (V

_{SDj}× α

_{SD})/80 = 1.0 − (V

_{SDj}/37.6).

_{FC}− 1)/2) × (0.82 + 0.18V

_{CD}) × (1 − 0.16 × (V

_{PL}− 1)/2) × (1 − V

_{AD}/222.2) × (1 − V

_{SD}/37.6),

_{FC}is the road function (main arterial road: 1; auxiliary arterial road: 2; collector road: 3), V

_{CD}is the existence of a median strip (existence: 1; absence: 0), VPL is the parking density (low: 1; mid: 2; high: 3), V

_{AD}is the number of access points per km, and V

_{SD}is the number of traffic breaks per km.

#### 3.5. Weighting Factors Verification

_{i}) is assumed in Equation (10) and a natural logarithm is taken, verification through multiple linear regression analysis is possible. To calculate the weight, the above equation is converted into a log function form according to the variables of this study, as shown in Equation (11):

_{fc}× ln[1 − 0.13 × (V

_{FC}− 1)/2]) + (w

_{CD}× ln[0.82 + 0.18V

_{CD}]) + (w

_{PL}× ln[1 − 0.16 × (V

_{PL}− 1)/2]) + (w

_{AD}× ln[1 − V

_{AD}/222.2]) + (w

_{sd}× ln[1 − V

_{SD}/37.6]).

_{i}) as the independent variable. The F-value and coefficient of determination (R2) were analyzed to examine the significance of the independent variable at the 0.05 level, find the correlation between variables, and determine whether the model is useful.

## 4. Estimation of Traffic Accident Reduction Effect and Application Method

#### 4.1. Speed Limit Test and Comparison

#### 4.2. Estimation of Accident Reduction Effect

^{1.2228},

#### 4.3. Field Application Method

## 5. Conclusions and Outlook

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 13.**Adjustment factor development (

**a**) and standardization for the existence (

**b**) of road function.

**Figure 14.**Adjustment factor development (

**a**) and standardization for the existence (

**b**) of the median strip.

**Figure 15.**Adjustment factor development (

**a**) and standardization for the existence (

**b**) of the level of parking.

**Figure 16.**Adjustment factor development (

**a**) and standardization for the existence (

**b**) of the number of access density.

**Figure 17.**Adjustment factor development (

**a**) and standardization for the existence (

**b**) of the number of traffic breaks.

**Figure 19.**Relationship between changing the speed limit and the subsequent change in mean driving speed.

**Figure 20.**Simple linear relationship between the relative change in speed and the corresponding change in accident frequency.

Classification | Maximum Speed | Minimum Speed |
---|---|---|

General road: 1 lane direction | 60 km/h | - |

General road: more than 2 lane directions | 80 km/h | - |

Expressway | 90 km/h | 30 km/h |

Freeway: 1 lane direction | 80 km/h | 50 km/h |

Freeway: more than 2 lane directions | 100 km/h | 50 km/h |

Freeway: designated by metropolitan | 120 km/h | 50 km/h |

Country | Urban Arterial (km/h) | Local and Collector Roads (km/h) | Country | Urban Arterial (km/h) | Local and Collector Roads (km/h) |
---|---|---|---|---|---|

USA | 48~88 | 40~56 | Mexico | 80 | 20–60 |

Australia | 60–70–80 | 50 | Netherlands | 50–70 | 50 |

Austria | 50 | 50, 40 (residential) | New Zealand | 50–80 | 50 |

Canada | 50–60 | 40–50 | Norway | 50 | 30–50 |

Czech Republic | 50–60 | 50 | Poland | 50 | 50 |

Denmark | 50 | 50 | Portugal | 50–90 | 50 |

Finland | 50 | 30–40–50 | Russia | 60 | 60 |

France | 50 | 30–50 | Sweden | 50–70 | 30–50 |

Germany | 50 | 50 | Switzerland | 50 | 50 |

Greece | 50–70–90 | 40–50 (collector), 30 (local) | United Kingdom | 48–64 | 32–48 |

Iceland | 50–60 | 50 (collector), 30 (local) | South Korea | 60 (1 lane direction), 80 (more than 2 lane directions) | |

Ireland | 50–80 | 50 |

Road Type | Speed Limits, mph (km/h) |
---|---|

Roads with a mix of motorized and unprotected road users (i.e., pedestrians and cyclists) | 20 (30) |

Roads with uncontrolled access where side impact crashes can occur | 30 (50) |

Undivided roads where head-on crashes can occur | 45 (70) |

Controlled access facilities with a physical median separation where at-grade access and non-motorized road users are prohibited | >60 (>100) |

Approach | Data Required | Advantages and Disadvantages |
---|---|---|

Engineering (operating speed) | The existing speed profile as well as data on accesses, pedestrian/bicycle traffic, curbside parking, etc. | [+] Does not place an undue burden on enforcement, and provides residents and businesses with a valid indication of actual travel speeds.[−] Speed limits are often set lower than the 85th percentile speed. |

Engineering (road risk) | Functional classification of the road, setting (urban/rural), surrounding land uses, access, design features of the road. | [+] The speed limit and the function of the road are aligned.[−] The road risk methods may result in speed limits that are well below the 85th percentile speeds, resulting in an increased burden on enforcement if remedial measures are not employed (i.e., traffic calming, etc.). |

Optimal speed limits | Cost models and input data to account for air pollution, crashes, delay, etc. | [+] Provides a balanced approach to setting speed limits that is considerate of many (if not all) of the impacts that speed has on society.[−] Data collection and prediction models may be difficult to develop and are subject to controversy among professionals. |

Expert system | Data needs depend on the system, but generally require the same data as used in the engineering approaches. | [+] A systematic and consistent method of examining and weighing factors other than vehicle operating speeds. It provides consistency in setting speed limits within a jurisdiction.[+] Practitioners can rely on only outputs from the expert system without a review of the results. |

Injury minimization | Crash types and patterns for different road types and survivability rates for different operating speeds. | [+] There is a sound scientific link between speed limits and serious crash prevention. Places a high priority on road safety.[−] This method is based solely on a road safety premise and may not be accepted as appropriate in some jurisdictions. |

Speed Data Collection on Based Probe Vehicles | Year-Month -Date-Time | Link ID | Speed (km/h) |
---|---|---|---|

↓ | 2016-04-04-00 | 1100006802 | 88 |

Removal of error data | 2016-04-04-00 | 1100005706 | 50 |

↓ | 2016-04-04-00 | 1100004900 | 68 |

Timetable building (per hour) | 2016-04-04-00 | 1100032704 | 35 |

Classification | Number of Road Sections | Total Sections Distance (km) |
---|---|---|

Gangnam | 124 | 294.6 |

Gangbuk | 91 | 214.0 |

Sum | 215 | 508.6 |

Variable (1st Level) | Range | Variable Aggregation | ||
---|---|---|---|---|

2nd Level | 3rd Level | 4th Level | ||

Posted speed limit | 40–70 km/h | |||

Functional classification | 1 (major arterial, 44.5%), 2 (minor arterial, 33.3%), 3 (collector, 22.2%) | |||

Land use | 1 (residential), 2 (commercial), 3 (industrial) | |||

Roadside development | 1 (high), 2 (mid), 3 (low) | |||

Median strip | 1 (divided), 0 (none) | |||

Number of lanes | 4–10 lanes | |||

Lane width | 1 (>3.3 m), 0 (<3.3 m) | |||

Parking density | 1 (low), 2 (mid), 3 (high) | |||

Sidewalk width | 1 (>3.0 m), 0 (<3.0 m), n/a (none) | |||

Number of signalized intersections | per km | Number of signalized facilities | Number of discontinuity facilities | Number of all inter- interruptions |

Number of signalized crosswalks | per km | |||

Number of non-signalized intersections | per km | Number of non-signalized facilities | ||

Number of non-signalized crosswalks | per km | |||

Number of driveways | per km | Total number of accesses | ||

Number of entrances (a structure) | per km | |||

Number of left turning bays | per km | Total number of turning bays | ||

Number of right turning bays | per km | |||

Number of speed limit signs | per km | Total number of signs | ||

Number of other traffic signs | per km | |||

Number of speed cameras | per km | |||

Number of bus stops | per km | |||

Bus lane | 1 (existence), 0 (nothing) |

Correlation Coefficient | Interpretation of Correlation |
---|---|

0.0–0.2 | Very low correlation |

0.2–0.4 | Low correlation |

0.4–0.6 | Some correlation |

0.6–0.8 | High correlation |

0.8–1.0 | Very high correlation |

85% Speed | Road Function | Number of Lanes | Roadside Development | Existence of Median | Lane Width | Level of Parking | Signalized Intersection | Non-Signalized Intersection | Number of Signalized Breaks | Number of Non-Signalized Breaks | Bus Stop | Building Entry/Exit | All Accesses | Total Number of Traffic Breaks | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

85% speed | 1.0 | −0.489 | 0.486 | 0.407 | 0.620 | 0.424 | −0.421 | −0.674 | −0.512 | −0.729 | −0.499 | −0.743 | −0.591 | −0.527 | −0.745 |

0.003 | 0.003 | 0.014 | <0.0001 | 0.010 | 0.011 | <0.0001 | 0.001 | <0.0001 | 0.002 | <0.0001 | 0.000 | 0.001 | <0.0001 | ||

Road function | −0.489 | 1.0 | −0.763 | 0.146 | −0.295 | −0.298 | −0.194 | 0.734 | 0.684 | 0.668 | 0.673 | 0.305 | 0.136 | −0.094 | 0.807 |

0.003 | <0.0001 | 0.395 | 0.080 | 0.077 | 0.257 | <0.0001 | <0.0001 | <0.0001 | <0.0001 | 0.070 | 0.430 | 0.587 | <0.0001 | ||

Number of lanes | 0.486 | −0.763 | 1.0 | −0.100 | 0.418 | 0.383 | −0.174 | −0.712 | −0.474 | −0.582 | −0.465 | −0.433 | −0.320 | −0.128 | −0.633 |

0.003 | <0.0001 | 0.562 | 0.011 | 0.021 | 0.309 | <0.0001 | 0.004 | 0.000 | 0.004 | 0.008 | 0.058 | 0.456 | <0.0001 | ||

Roadside development | 0.407 | 0.146 | −0.100 | 1.0 | 0.346 | 0.349 | −0.454 | −0.085 | 0.044 | −0.304 | 0.051 | −0.313 | −0.407 | −0.577 | −0.159 |

0.014 | 0.395 | 0.562 | 0.039 | 0.037 | 0.005 | 0.621 | 0.798 | 0.072 | 0.767 | 0.063 | 0.014 | 0.000 | 0.354 | ||

Existence of median | 0.620 | −0.295 | 0.418 | 0.346 | 1.0 | 0.460 | −0.184 | −0.484 | −0.187 | −0.608 | −0.184 | −0.535 | −0.371 | −0.417 | −0.486 |

<0.0001 | 0.080 | 0.011 | 0.039 | 0.005 | 0.283 | 0.003 | 0.274 | <0.0001 | 0.283 | 0.001 | 0.026 | 0.011 | 0.003 | ||

Lane width | 0.424 | −0.298 | 0.383 | 0.349 | 0.460 | 1.0 | −0.138 | −0.369 | −0.141 | −0.472 | −0.138 | −0.169 | −0.223 | −0.223 | −0.375 |

0.010 | 0.077 | 0.021 | 0.037 | 0.005 | 0.422 | 0.027 | 0.413 | 0.004 | 0.422 | 0.325 | 0.190 | 0.190 | 0.024 | ||

Level of parking | −0.421 | −0.194 | −0.174 | −0.454 | −0.184 | −0.138 | 1.0 | 0.095 | −0.003 | 0.172 | −0.001 | 0.484 | 0.421 | 0.552 | 0.106 |

0.011 | 0.257 | 0.309 | 0.005 | 0.283 | 0.422 | 0.582 | 0.984 | 0.315 | 0.996 | 0.003 | 0.011 | 0.001 | 0.538 | ||

Signalized intersection | −0.674 | 0.734 | −0.712 | −0.085 | −0.484 | −0.369 | 0.095 | 1.0 | 0.446 | 0.843 | 0.419 | 0.658 | 0.407 | 0.236 | 0.769 |

<0.0001 | <0.0001 | <0.0001 | 0.621 | 0.003 | 0.027 | 0.582 | 0.006 | <0.0001 | 0.011 | <0.0001 | 0.014 | 0.166 | <0.0001 | ||

Non-signalized intersection | −0.512 | 0.684 | −0.474 | 0.044 | −0.187 | −0.141 | −0.003 | 0.446 | 1.0 | 0.400 | 0.998 | 0.304 | 0.082 | −0.079 | 0.826 |

0.001 | <0.0001 | 0.004 | 0.798 | 0.274 | 0.413 | 0.984 | 0.006 | 0.016 | <0.0001 | 0.071 | 0.636 | 0.649 | <0.0001 | ||

Number of signalized breaks | −0.729 | 0.668 | −0.582 | −0.304 | −0.608 | −0.472 | 0.172 | 0.843 | 0.400 | 1.0 | 0.382 | 0.664 | 0.471 | 0.410 | 0.846 |

<0.0001 | <0.0001 | 0.000 | 0.072 | <0.0001 | 0.004 | 0.315 | <0.0001 | 0.016 | 0.021 | <0.0001 | 0.004 | 0.013 | <0.0001 | ||

Number of non-signalized breaks | −0.499 | 0.673 | −0.465 | 0.051 | −0.184 | −0.138 | −0.001 | 0.419 | 0.998 | 0.382 | 1.0 | 0.282 | 0.066 | −0.092 | 0.816 |

0.002 | <0.0001 | 0.004 | 0.767 | 0.283 | 0.422 | 0.996 | 0.011 | <0.0001 | 0.021 | 0.096 | 0.701 | 0.595 | <0.0001 | ||

Bus stop | −0.743 | 0.305 | −0.433 | −0.313 | −0.535 | −0.169 | 0.484 | 0.658 | 0.304 | 0.664 | 0.282 | 1.0 | 0.698 | 0.639 | 0.577 |

<0.0001 | 0.070 | 0.008 | 0.063 | 0.001 | 0.325 | 0.003 | <0.0001 | 0.071 | <0.0001 | 0.096 | <0.0001 | <0.0001 | 0.000 | ||

Building access | −0.591 | 0.136 | −0.320 | −0.407 | −0.371 | −0.223 | 0.421 | 0.407 | 0.082 | 0.471 | 0.066 | 0.698 | 1.0 | 0.925 | 0.332 |

0.000 | 0.430 | 0.058 | 0.014 | 0.026 | 0.190 | 0.011 | 0.014 | 0.636 | 0.004 | 0.701 | <0.0001 | <0.0001 | 0.048 | ||

All accesses | −0.527 | −0.094 | −0.128 | −0.577 | −0.417 | −0.223 | 0.552 | 0.236 | −0.079 | 0.410 | −0.092 | 0.639 | 0.925 | 1.0 | 0.203 |

0.001 | 0.587 | 0.456 | 0.000 | 0.011 | 0.190 | 0.001 | 0.166 | 0.649 | 0.013 | 0.595 | <0.0001 | <0.0001 | 0.236 | ||

Total number of traffic breaks | −0.745 | 0.807 | −0.633 | −0.159 | −0.486 | −0.375 | 0.106 | 0.769 | 0.826 | 0.846 | 0.816 | 0.577 | 0.332 | 0.203 | 1.0 |

<0.0001 | <0.0001 | <0.0001 | 0.354 | 0.0026 | 0.0242 | 0.5377 | <0.0001 | <0.0001 | <0.0001 | <0.0001 | 0.0002 | 0.048 | 0.2355 |

Variables | R^{2} | Degree of Freedom | F-Value | p-Value | Constant | Regression Coefficient |
---|---|---|---|---|---|---|

Road function | 0.240 | 35 | 10.71 | 0.002 | 61.10 | −5.11 |

Median strip | 0.384 | 35 | 21.18 | 0.000 | 49.98 | 14.68 |

Degree of parking | 0.177 | 35 | 7.33 | 0.011 | 59.91 | −6.31 |

Number of accesses | 0.278 | 35 | 13.07 | 0.001 | 58.63 | −0.36 |

Total number of traffic breaks | 0.555 | 35 | 42.35 | 0.000 | 62.53 | −2.13 |

Variables Name | Code | Variables Name | Code |
---|---|---|---|

Road Functional Class | RFC | Central Divide | CD |

Parking Level | PL | Signal Density | SD |

Access Density | AD |

ln(Variable Name) | DF | Regression Coefficient | Standard Error | t-Value | Significance Probability (Pr > |t|) | Variance Inflation Factor (VIF) |
---|---|---|---|---|---|---|

constant value | 1 | 0.000 | 0.000 | −1.010 | 0.321 | 0.000 |

FC | 1 | 0.995 | 0.003 | 286.140 | <0.0001 | 0.249 |

CD | 1 | 1.000 | 0.002 | 610.040 | <0.0001 | 0.317 |

PL | 1 | 1.001 | 0.003 | 399.980 | <0.0001 | 0.217 |

AD | 1 | 0.998 | 0.002 | 500.370 | <0.0001 | 0.284 |

SD | 1 | 1.006 | 0.002 | 471.330 | <0.0001 | 0.422 |

**Table 13.**Two-sample t-test for the speed difference (V85-RSL) between the high and low accident rate groups.

Method | DF | t Value | Pr > |t| | Variance |
---|---|---|---|---|

Pooled | 70 | 1.74 | 0.086 | equal |

Satterthwaite | 63.71 | 1.84 | 0.071 | unequal |

Cochran | 1.84 | 0.075 |

Step | Data Required | Notes |
---|---|---|

Target segment setting | Directional urban road with two or more lanes (except two lanes both sides or free flow roads) | Minimum length 800 m |

Collecting data | Road function, whether there is an absence of a median strip (50% or more facility separation), average lane width over 3.3 m), driveways and entrances to buildings, the number of signal or no signal intersections and crossings | Both sides data per km |

Setting speed limits | Model application calculation | Rounding (10 km/h) |

Field application | Connection road speed limit, geometry | Multiple accident roads |

Speed Limit (km/h) | FC | CD | PL | SD | AD |
---|---|---|---|---|---|

70 | Main arterial road | with | 0 | 0 | 0 |

60 | Main arterial road | none | 0 | 0 | 0 |

50 | Main arterial road | none | 1 | 1 | 1 |

50 | Auxiliary arterial road | none | 0 | 1 | 1 |

40 | Main arterial road | none | 2 | 2 | 1 |

40 | Auxiliary arterial road | none | 1 | 2 | 2 |

40 | Collector road | none | 0 | 2 | 1 |

30 | Collector road | none | 1 | 2 | 2 |

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**MDPI and ACS Style**

Kim, H.; Jung, D.
Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data. *Appl. Sci.* **2021**, *11*, 5710.
https://doi.org/10.3390/app11125710

**AMA Style**

Kim H, Jung D.
Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data. *Applied Sciences*. 2021; 11(12):5710.
https://doi.org/10.3390/app11125710

**Chicago/Turabian Style**

Kim, Hyungkyu, and Doyoung Jung.
2021. "Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data" *Applied Sciences* 11, no. 12: 5710.
https://doi.org/10.3390/app11125710