demandlocker_TRB_v3

Towards an AVL-based
Demand Estimation Model
𝒂
Luis Moreira-Matias,
𝒃
Oded Cats
𝒂
Intelligent Transport Systems Group
Social Solutions Research division
NEC Laboratories Europe, Heidelberg, DE
𝒃
Dep. Transport and Planning, TU Delft, NL

Outline
 Problem Overview
 Methodology
 Application
 Keynotes

3 © NEC Corporation 2015
▌ Demand Information is critical for estimating the overall demand profile of a network;
Methods for Demand/Load Estimation
Surveys
AVL data APC data
Video FootagesWeight Sensors

Problem Overview
▌Methods vs. Shortcomings
Video Footages, Weight Sensors
• Inaccurate;
• Expensive;
• Offline;
APC data:
• Incomplete;
• Offline;
• Inaccessible (e.g. 3rd party);
Surveys:
• Offline;
• Expensive;
• Slow;
AVL data:
• Commonly Used with APC data;
• Inaccurate;
▌Applications:
Tactical Planning
Real-Time Control/Monitoring
Route Concession Management

Problem Overview
▌Methods vs. Shortcomings
Video Footages, Weight Sensors
• Inaccurate;
• Expensive;
• Offline;
APC data:
• Incomplete;
• Offline;
• Inaccessible (e.g. 3rd party);
Surveys:
• Offline;
• Expensive;
• Slow;
AVL data:
• Commonly Used with APC data;
• Inaccurate;
▌Applications:
Real-Time Control/Monitoring

Research Question
▌Is it possible to use standalone AVL data to
estimate real-world passenger loads in a public
transportation network?
AVL data
Data Analytics
Passenger
Load Profiles

Methodology

(A) High-Level Demand Estimation

(A) High-Level Demand Estimation
Parameters and
Assumptions
Original Schedule
• Equal Vehicle Cap.
• Desired Occupancy
• Frequency Defined
through the Max. Load
Point method
AVL-based
headway deviations
Theoretical
Maximum Load
Period-Based
Maximum Load
Estimation

(B) Dwell-Time Decomposition

(B) Dwell-Time Decomposition
Assumptions
AVL-based
Dwell Times
• No Alightings on the first stops
• No Boardings on the last stops
Dwell Formula’s
Constants
computed using
Linear Regression
Walding’s
Dwell Time
Decomposition
• Boarding Time per pax
• Alighting Time per pax
• Deadtime/Delay

(C,D) Trip Load Profile Estimation

(C,D) Trip Load Profile Estimation (1/2)
Assumptions
AVL-based
Estimated Loads
• The stop with the largest
dwell time is considered as
maximum load point;
• Alternatively, the stop where
the cumulative dwells
achieve half of the trip’s
total dwell is selected
instead;
Local Regression
using LOESS
• Estimated Loads serve as
ground truth;
• A dependence function is
inferred for each trip using
the measured dwells and the
previously calculated dwell
constants;

(C,D) Trip Load Profile Estimation (2/2)
Incremental Output
Filtering
• Dwell time
constants are used
to determine load’s
confidence
intervals;
• A Linear
Progressive Load
Function is
empirically
defined;
Normalization
• LOESS results are
normalized to the
range of the
resulting
confidence
interval;
Trip Load Estimation

Local Regression (LOESS) in a Nutshell
• LOESS approximates non-linear
functions by combining multiple linear
ones;
• A linear function is created for each
data point defined by a sample in our
feature space;
• They are learned based only on a
subset of neighbors (check fig.);
LOESS Example

LOESS on estimating Vehicle Loads (example) 1/2
Target Function is
non-linear

LOESS on estimating Vehicle Loads (example) 2/2
Target Function is
non-linear but...
... Resulting
Function is
Globally Non-
Linear But
Locally Linear!

(E) Typical Load Profile Estimation

(E) Typical Load Profile Estimation
Trip-based (Weak)
Load Estimations
Stop-based Median
Loads (unrealistic profile)
Nearest Neighbor
(w/ Euclidean Distance)
Typical Load Profile

Experiments - Case Study
• AVL data from a bus fleet in Dublin, Ireland;
• Population: 1.3M inhabitants;
• Scenario with buses, heavy and light rail services;
• Communication granularity: 15-seconds;
• Data about two specific routes (i.e. 13, 140);
• 140: Commuter Line; 13: Airport Line:
• Planned headway in Peak Hours: 10-20 minutes;
• High Headway Variability;
• Missing data ratio: 10%;
• 2 Peak periods: 8am-12am/4pm-8pm;
• 140 Avg. Dwell Time: 𝟏𝟏. 𝟎𝟐 ± 𝟑𝟕. 𝟒𝟗 sec.
• 13 Avg. Dwell Time: 𝟏𝟎. 𝟎𝟐 ± 𝟓𝟗. 𝟒𝟑 sec.
• No APC data is available;

Experiments - Results
Example of the load profile for a selected trip;
Dwell Times
Estimated Loads
Load’s Confidence Interval
Stops

Experiments – Results for route 140 (Morning)

Experiments – Results for route 140 (Evening)

Experiments – Results for route 13 (Morning)

Experiments – Results for route 13 (Evening)

Final Remarks
1. We proposed the first methodology to estimate bus loads
based on AVL data standalone;
2. It involves multiple steps, including frequency determination
methods and inference of dwell time coefficients through
constrained simple/local regression methods and an
incremental smoothing based on the passenger flow
expectation;
3. Key performance indicators can be derived from it in real-
time (e.g. vehicle utilization rate, empty-seat/exceeded load
running distance);

Future Work
1. A proper validation against a fully reliable ground truth
(e.g. APC) is still required to assess the method´s true
potential;
2. Maximum Load Point Selection can be improved using Fuzzy
Logic;
3. The concept of neighboring can also be defined in a temporal
dimension (e.g. by using a sliding window or other forgetting
mechanisms) to consider seasonal fluctuations;

Thank you for your time!
luis.matias@neclab.eu

demandlocker_TRB_v3

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