2015-12-04 Overview & Requirements Gathering

MBTA Route Performance Analysis
Rider ExperienceSchedule Analysis Vehicle Optimization

Agenda
1. Problem Statement: Transportation Issues
2. Data, What it can/cannot tell us
3. Platform Overview
4. Early Observations
5. Early Insights
6. Early Recommendations to MBTA

1. Problem Statement: Transportation Issues
• Waits can be long
• Buses not always showing up on schedule
• Buses sometimes stacked one-after-another
which is wasteful
• How much, how often, and why?

2. Data, What it can tell us
"route_id": "71",
"mode_name": "Bus",
"direction_name": "Inbound",
"trip_id": "28346992",
"trip_name": "9:30 pm from Watertown Sq …",
"vehicle_id": "y4128",
"vehicle_lat": "42.3747940063477",
"vehicle_lon": "-71.1326065063477",
"vehicle_bearing": "85",
"vehicle_timestamp": "1447988172"
11/19/2015
9:56:12 PM
Every 60 seconds, each bus reports its position
These dots represent the 20+ pings of a typical trip of the Route 71 bus

route_id: 71
INDEX: 42
trip_id: 28346775
stop_id:
stop_sequence:
vehicle_id: y4121
vehicle_lat: 42.3740653991699
vehicle_lon: -71.1249313354492
vehicle_timestamp: 1444948220
route_id: 71
INDEX: 41
trip_id: 28346775
stop_id: 2073
stop_sequence: 21
vehicle_id: y4121
vehicle_lat: 42.373956
vehicle_lon: -71.124752
route_id: 71
INDEX: 43
trip_id: 28346775
stop_id: 2074
stop_sequence: 22
vehicle_id: y4121
vehicle_lat: 42.373356
vehicle_lon: -71.123226
By portioning time according to
distance, we can estimate when
the bus was at the stop.

StopsStopsStopsStopsStopsBus DataBus DataBus DataBus Data

2. Data, What it cannot tell us
We don’t know how many people are on the bus:
- Can’t tell whether riders are getting left at the curb (full bus)
- Can’t tell the busy stops from the light stops
- Can’t understand utilization by segment

Web-based “SaaS” solution
- Add/Del Tracked Routes
- Summary Scorecard
- Detailed analysis
- Schedule Variance
- Schedule Viability
- Wait time Distribution
- Vehicle Deployment Policy

Scorecard, comprised of metrics, so that
• Best performing routes can be learned from,
• Worst performing routes can be addressed

Heat map to show Schedule Variance Distribution to show Wait Times
Maps to show Bus Locations Logs to validate charts

DONE
http://realtime.mbta.com/portal
current_location raw_location
Fetcher()
trip_actuals
raw_to_trip()
trip_variance
compute_trip_variance()

GTIF
Schedules
5:13 am from Watertown Sq Terminal to Waterhouse St @ Massachusetts Ave
8:48 am from Watertown Sq Terminal to Harvard Upper Busway @ Red Line
Printed Schedule Published GTIF Schedule (Database)

Printed schedule not the same as the operations schedule.

Frequency
0
earlier 0
15 to 20 min early 1
+/- 2 min 699
2 to 5 min late 617
5 to 10 min late 397
later 0
Early Late
According to Schedule …
Route 71 is frequently early or late, too-often it is very late.

Route 71 wait time (time between buses) can be significant.
Frequency
no wait 4
0-5 mins 1099
5-10 mins 809
10-15 mins 761
15-20 mins 466
20-25 mins 313
25-30 mins 169
30-35 mins 53
35-40 mins 51
40-45 mins 27
45-50 mins 5
50 mins or more 8
Min 0:00:00
Max 1:56:45
AVG 0:11:24
Median 0:09:50
29% > 15 min wait

4. Early ObservationsMorningRushEveningRush
10/19/2015 5:37
10/19/2015 5:57
10/19/2015 6:05
10/19/2015 6:11
10/19/2015 6:17
10/19/2015 6:31
10/19/2015 6:48
10/19/2015 6:41
10/19/2015 6:55
10/19/2015 7:02
10/19/2015 7:09
10/19/2015 7:16
10/19/2015 7:24
10/19/2015 7:30 <10 min
10/19/2015 7:42
10/19/2015 7:56
10/19/2015 8:12
10/19/2015 8:04
10/19/2015 8:26
10/19/2015 8:19
10/19/2015 8:33
10/19/2015 8:43
10/19/2015 8:48
10/19/2015 8:58
10/19/2015 9:18
10/19/2015 9:08
10/19/2015 9:30
10/19/2015 9:42
10/19/2015 9:55 <15 min
10/19/2015 10:25
10/19/2015 10:40
10/19/2015 11:10
10/19/2015 11:25
10/19/2015 11:40
10/19/2015 11:55
10/19/2015 12:08
10/19/2015 12:23
10/19/2015 12:38
10/19/2015 12:53
10/19/2015 13:08
10/19/2015 13:23
10/19/2015 13:38
10/19/2015 13:53
10/19/2015 14:06
10/19/2015 14:14
10/19/2015 14:24
10/19/2015 14:43
10/19/2015 14:51
10/19/2015 14:59
10/19/2015 15:10
10/19/2015 15:17
10/19/2015 15:28
10/19/2015 15:37 <10 min
10/19/2015 15:55
10/19/2015 16:04
10/19/2015 16:12
10/19/2015 16:33
10/19/2015 16:24
10/19/2015 16:43
10/19/2015 17:00
10/19/2015 17:10
10/19/2015 17:37
10/19/2015 17:19
10/19/2015 17:46
10/19/2015 18:04
10/19/2015 17:55
10/19/2015 18:13
10/19/2015 18:22
10/19/2015 18:40
10/19/2015 18:31
10/19/2015 18:49
10/19/2015 19:00
10/19/2015 19:22
10/19/2015 19:36
10/19/2015 19:50
Worst waits are
at peak periods!

TRIP TIMES
min 0:15:37
max 2:50:06
avg 0:27:12
median 0:25:19
End-to-end trip times vary widely, but
averages are not sensitive to changing
conditions of the day … we need to
observe more closely
Outbound, Total Time
Inbound, Total Time

4. Early Insights
End-to-end trip times vary widely,
depending on time of day. Schedules
could be aligned more closely to the
reality of traffic on the route throughout
the day.

End-to-end trip times vary widely,
depending on time of day. Schedules
could be aligned more closely to the
reality of traffic on the route throughout
the day.

A second look at the day reveals that
schedule is overly aggressive and can’t be
met.
Either schedule should be reduced to
meet equipment or equipment should be
increased to meet schedule.
>
Late more often than Early
Not enough equipment to meet Schedule

4. Summary of Early Observations
• Insufficient buses to meet 71 schedule
– Fall behind during peek periods
– Almost catch up during lull periods
– Missed Trips, 17 of 630 on 10/19/15 = 2.7%
• Propensity for Buses to cluster
– Un-uniform Wait Times
• Very short waits or very long waits
• Route 71 Schedule is a fallacy
– Printed schedule inconsistent with GTIF
– Buses are simply looping as fast as they can
– Number of vehicles governs availability

5. Early Insights
Optimizing for equipment utilization, looping as fast as possible, results in
vehicle clustering, short waits followed by long waits (time between buses).
Thesis:
Equilibrium
Uniform Wait Time
Uniform Occupancy
Uniform Velocity

5. Early Insights
Thesis:
Out of
Equilibrium
Short Wait Time
Lower Bus Occupancy
Bus speeds up with fewer riders

5. Early Insights
Thesis:
Out of
Equilibrium
Longer Wait Time
Higher Bus Occupancy
Bus Slows down with increased riders

5. Early Insights
Thesis:
Out of
Equilibrium
Un-Uniform Wait Times irritating riders
Un-Uniform Occupancy reducing utilization
Speed impacted, exacerbates clustering

• Align schedules, printed and GTIF/internal
• Set realistic schedules based on analysis
• Space vehicles more uniformly to reduce maximum rider wait
times
– Requires thought to balance schedule with rider experience – should
be “tuned” through A/B testing
• Send location update when bus at stop and door open
– Improve accuracy of future analyses
• Add Ridership data to feed
– # people (or weight of vehicle for approximation)
– Anonymized load/unload so individual rider trips can be analyzed

• Can’t Accelerate Slow Bus
– Road Speed can’t be influenced
– Skipping stops will anger waiting riders
• Only options is to decelerate Fast Bus
– Bus waits at stops to maintain spacing
Maintaining equilibrium

“Uber-fication” of buses – a connected app managing network of vehicles
Wait at Stop
Go when able

• Slowing bus
– Will reduce number of feasible trips and
scheduled time between buses
– Will improve uniformity of wait time
– Likely to improve utilization rate (fewer over-
crowded buses, few people left at curb)
– Likely to improve overall rider satisfaction

2015-12-04 Overview & Requirements Gathering

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Editor's Notes