FORECASTING BIKE TRAFFIC FOR BETTER TRAFFIC MANAGEMENT IN OTTAWA CITY

Study Group B8Gideon James Draviam - 61910680

Jasmeet Singh - 61910021

Kuheli Jati - 61910343

Mridul Mishra - 61910569

Rhishabh Garg - 61910352

Ujjwal Kejriwal - 61910587

FCAS Project

Forecasting Bike Traffic in Ottawa City to understand the requirement of increasing bike corridors at important junctions

Ottawa Traffic Department wants to estimate the bike traffic at 6 important junctions in 2019

Stakeholders and Problem Why is it needed?

• Ottawa Police documented 23% increase in road accidents• Registered vehicles went up by about 15,000, while number of

new drivers increased by approximately 5,000.

Data source: Streets of Ottawa, Ontario Canada (Kaggle)

“…look out for pedestrians, cyclists and motorcyclists” - Staff Sgt. Frank D’Aoust

Source: Globalnews.ca

Forecasting exercise will help the department in both short term and long term ways

Predict the maximum bike traffic in 2019 (Nov 2018 to Oct 2019)

One-time exercise as building corridors takes time and is cost-intensive

Model to be deployed as a current time forecast and not running forecast

Other Short Term Outcomes• Manage traffic management personnel

based on predicted forecasts

• Make-shift arrangements to increase bike

lanes in case of non-consistent forecasts

Cost Trade-off• Cost of building bike corridors (long-term) or

deploying higher manpower (short-term)

• Cost of accidents/life due to high traffic

$2M/km $9M/person

Source: C40 Cities Source: The Globalist

Understanding the data shows us the trend and weekly seasonality

5 years of data from Nov 2013 to Oct 2018

Date: The date in ISO-8601 format

COBY: NCC Eastern Canal Pathway

LMET: Laurier Segregated Bike lane

OBVW: O-Train Pathway just north of Bayview Station

OGLD: O-Train Pathway just north of Galdstone Avenue

ORPY: NCC Ottawa River Pathway

SOMO: Somerset bridge

All Counters

• Level and Increasing trend exists in the data (expected as population rises and vehicle

ownership rises)

• Data has high fluctuations which will lead to high noise

• Seasonality exists as can be observed from monthly and weekly plots

• Weekday seasonality was also observed

Trend

Seasonality

Data imputation using nearest average and aggregated at a weekly level to meet business objective

• Some series had missing data because of counter under maintenance (mainly winter season)

• Imputed with average of nearest neighbours

• Objective is to forecast the maximum bike traffic in the next year

• Data was aggregated at weekly level using the ‘maximum’ function which helped reduce noise

Extreme fluctuations in data leading to high noise

Relevant models were tried based on time series and business objective

Smoothening Regression

Seasonal NaïveMoving Average

Simple Exponential

Smoothening

Double Exponential

Smoothening

Holt’s Winters Additive

Holt’s Winter’s Multiplicative

Linear (T + Weekly

Index)

Linear (T + T^2 +

Weekly Index)

Logarithmic (T + Weekly

Index)AR Model

Level

Trend

Season

Auto-Correlation

Business Objective (No Running Forecast)

Partitioning, Benchmark, Metric of Interest & Comparison

Partitioning - Of the 5 years, 4 years data is TRAIN data and 1 year data is VALIDATION data

The Validation performance would be evaluated with 2 years also to negate isolated instances affecting the performance metric of the model

TRAIN

2 Year Validation

1 Year Validation

Benchmark - Seasonal Naive is taken as the benchmark for evaluating the models Metrics of Interest - Since the data transformation signifies the peak performance, the error of the model (SSE, MSE, RMSE & MAPE) are considered as the metrics to evaluate modelsComparison - MAPE is metric of primary interest because of scale and congruence of series

Multiplicative Holt Winter Model provides better prediction!

Forecasting the next year for LMETMethod

Multiplicative Holt-Winter

Performance

External Factors - Weather - Traffic - Holidays - Growth

We recommend corridor extension at 3 major junctions

Long Term

Short Term

Compared 99th pctl of Forecasts v/s 99th pctl of historical data to estimate the capacity of current corridors1

Historical 99th Percentile

Forecasted 99th

PercentileDifference Decision

COBY 2846 2859 0.45% No

LMET 3478 2786 -19.91% No

OBVW 1653 2055 24.32% Yes

OGLD 1648 1695 2.85% Maybe

ORPY 3946 3741 -5.20% No

SOMO 1100 1195 8.64% Yes

Corridor Cost

Life Saved2

3 x 5 km x $2M/km

1. Based on inputs from city planner in Pune2. Total 8 fatal accidents (2-wheeler) happened in 2017 . In 2010, multiple such measures were taken which brought down the rate by 50%. Assumed 40% reduction in death toll because

of corridor extension

$30M

$29M

= =

=8 lives x 40%x $9M/life

=

Since Lane Construction is a long-term project, Ottawa Traffic

Department can make temporary lanes (from roads) to better

manage traffic

THANKSThank You.