Nicolas Gomez - Measuring bus ride satisfaction from latent attributes
- 1. Service satisfaction of bus riders and mode shift intention Nicolas Gomez 1
- 2. 2 Motivation • Transit ridership is decreasing in most American cities. In New York, bus ridership went down 5.5% from 2016 to 2017 697 671 668 678 667 651 638 603 2010 2011 2012 2013 2014 2015 2016 2017 NYCT Annual Bus Ridership (millions)
- 3. • Studies in NYC have explained higher bus ridership with improvements in bus service: ◦ Real-time information ◦ Reliability and on-time performance ◦ Select Bus Service • But service is not improving: average bus speeds went down 4.5% in 2017-2018 • And these studies don’t consider attitudes or perceptions of service. • Where are those trips going and why are they opting out of the bus? 3 Motivation
- 4. Introduction • MTA Average weekday bus route ridership decreased in 2016-2017 ◦ 87% of bus routes have with less demand 4 Route 2012 2013 2014 2015 2016 2017 Δ (2016-2017) M86Lcl/SBS 25,431 25,484 23,846 23,576 24,746 20,247 -4,499 -18.20% M5 13,914 12,807 11,767 10,711 10,063 6,893 -3,170 -31.50% Bx1/2 38,334 38,786 39,549 38,411 36,487 33,374 -3,113 -8.50% Bx15 21,517 26,235 32,328 30,687 28,156 25,145 -3,011 -10.70% Bx19 31,561 31,954 31,434 31,531 29,378 26,454 -2,924 -10.00% M66 12,962 12,845 12,449 11,810 11,195 8,442 -2,753 -24.60% Bx36 30,618 31,566 31,745 31,729 30,474 27,786 -2,688 -8.80% Bx40/42 26,640 27,659 27,750 26,378 25,454 22,789 -2,665 -10.50% B44Lcl/SBS 39,661 37,786 36,016 37,021 37,418 34,877 -2,541 -6.80% Bx41Lcl/SBS 18,947 20,623 25,077 24,231 23,558 21,088 -2,470 -10.50% B15 22,384 22,729 22,132 22,153 22,282 19,827 -2,455 -11.00% M34/SBS 18,543 17,858 15,945 15,080 14,901 15,130 229 1.50% B70 6,074 6,059 5,394 5,387 5,942 6,229 287 4.80% Q20 13,427 13,609 13,452 13,371 14,192 14,577 385 2.70% B63 12,672 12,477 12,187 11,778 11,607 12,008 401 3.50% M23Lcl/SBS 14,898 14,546 13,971 12,716 12,308 13,212 904 7.30%
- 5. Introduction • MTA Average weekday bus route ridership decreased in 2016-2017 ◦ 87% of bus routes have with less demand • Only 25% of bus routes in NYC have an acceptable service level. (Transit Center / Bus Turnaround Coalition) • The project: ◦ Conduct a survey of known bus riders asking about satisfaction with different attributes of bus service ◦ Include a stated choice experiment with changed attributes of bus service ◦ Identify factors ◦ Estimate a DCM considering perception attributes 5
- 6. Proussaloglou, Koppelman (1989) Attitudes of commuter rail riders were used as explanatory variables in a mode choice model to determine contribution of perception and service improvements Kuppam, Pendyala, Rahman (1999) Perception of service as attributes has a higher contribution for explaining mode choice than only sociodemographic variables Popuri, Proussaloglou, Ayvalik, Koppelman, Lee (2011) Attitudinal factors related to daily travel improved goodness-of-fit of mode choice model in the Chicago Area. The study also highlights the importance of publicity for communicating transit service. Transportation Research Board (2013) Guidebook to measure user satisfaction and service quality of transit systems 6 Literature review
- 7. Wan et. al (2015) Survey of SBS route riders reflected an improvement in the perception of service quality and identified actions to improve traveler satisfaction Brakewood et. al (2015) Conducted a survey after SBS implementation and concluded that an increase of up to 100 trips on average weekday could be attributed to availability of real-time information. Tyndall et al (2018) Review of real-time bus location data concluded that ridership could increase when bus frequency, reliability and speeds were increased Hu et. Al (2015) Estimated a perception-based mode choice model in Nanjing, China evaluating traveler attributes and bus service perception and determined which factors can contribute in a higher ridership 7 Literature review
- 8. 8 Bus routes • At least two bus routes with varying attributes that have experienced a significant decrease in demand • Not substitutable with subway Route Type Δ 2016-2017 M86 SBS -4499 (-18%) Cross-town B44 Local / SBS -2541 (-6.8%) 20% shared with subway B46 Local / SBS -1677 (-3.9%) 25% shared with subway
- 9. • Respondents need to be recent bus riders • Short length (2-3 min) to ensure high response rate • Paper survey on field (in vehicles and in bus stops) • Online survey to minimize response time and incentivize reponses 9 Data collection
- 10. General attributes Characteristics of the bus trip Satisfaction of bus service Mode shift intention • Age • Gender • Bus route • Location (At bus / At stop) 10 Data collection
- 11. • Trip purpose • Frequency of taking the bus • Availability of other modes • Distance to bus stop • Waiting time at bus stop • Trip length • Transfer at end of trip 11 Data collection General attributes Characteristics of the bus trip Satisfaction of bus service Mode shift intention
- 12. • Overall satisfaction • Availability ◦ Distance to bus stop ◦ Convenience of payment ◦ Number of bus stops in bus route • Reliability ◦ Frequency of service ◦ On-time performance ◦ Boarding time ◦ Travel speed • Comfort ◦ Cleanliness of vehicles ◦ Comfort inside vehicles 12 Data collection General attributes Characteristics of the bus trip Satisfaction of bus service Mode shift intention
- 13. • Stated preference choice (taking the bus / switching to another mode) under three scenarios: ◦ “Your bus stop gets removed: you must walk (5- 12) more minutes to a stop” ◦ “Bus frequency decreases: you must wait (5-12) more minutes at the stop” ◦ “Travel speed decreases: you travel (5-12) more minutes in the bus” 13 Data collection General attributes Characteristics of the bus trip Satisfaction of bus service Mode shift intention
- 14. 14 Sample • Sample (Brakewood, MacFarlane and Watkins, 2015): 𝑠𝑠 = 𝑝𝑜𝑝 1 + 𝑝𝑜𝑝 − 1 𝑑2 𝑝 1 − 𝑝 𝑧 𝛼/2 2 Route Demand Representative Sample M86 20247 382B44 34877 B46 41876 Personal attributes Observations (Total sample = 57) Percentage Gender Male 30 53% Female 27 47% Age 16 – 25 13 23% 26 – 35 34 60% 36 – 55 10 17% Trip purpose Work 29 51% School 21 37% Entertainment 0 0 Errands 4 7% Visiting 3 5% Frequency Less than 1 day 5 9% 1-2 days per week 6 11% 3-4 days per week 11 19% 5+ days per week 35 61%
- 15. 15 Sample Personal attributes Observations (Total sample = 57) Percentage Gender Male 30 53% Female 27 47% Age 16 – 25 34 60% 26 – 35 13 23% 36 – 55 10 17% Trip purpose Work 29 51% School 21 37% Entertainment 0 0 Errands 4 7% Visiting 3 5% Frequency Less than 1 day 5 9% 1-2 days per week 6 11% 3-4 days per week 11 19% 5+ days per week 35 61% 0% 20% 40% 60% 80% 100% B46 B44 M86 Dissatisfied Neutral Satisfied Very Satisfied
- 16. • Exploratory factor analysis is used to estimate a model of proper latent (unobserved variables) from a series of service attributes. 𝐹𝑖 = 𝑛=1 𝑁 𝑥 𝑛 × 𝑐 𝑛𝑖 16 Factor analysis Reliability Convenience Comfort Frequency On-time Boarding Distance Payment Speed Bus stops Cleanliness Comfort Attribute F1 F2 F3 Distance Frequency 0.995 On-time 0.503 Payment Boarding 0.529 Cleanliness 0.709 Comfort 0.937 Speed 0.453 Stops 0.987 RMSR obtained: 0.04
- 17. • Mixed logit model from panel dataset ◦ 3 observations for each individual ◦ Compute trip length and evaluate significance in model • Binary choice ◦ Travel by bus ◦ Switch mode • Latent factors as attributes for mode shift intention 17 Mixed logit model Travel by bus Travel by other mode Your bus stop gets removed: you must walk 5 more minutes to a stop ( ) ( ) Walk ( ) Subway ( ) Private Car ( ) Taxi/Uber/Lyft ( ) Bicycle/Citibike ( ) Other: _______ Bus frequency decreases: you must wait 5 more minutes at the stop ( ) ( ) Walk ( ) Subway ( ) Private Car ( ) Taxi/Uber/Lyft ( ) Bicycle/Citibike ( ) Other: _______ Travel speed decreases: you travel 5 more minutes in the bus ( ) ( ) Walk ( ) Subway ( ) Private Car ( ) Taxi/Uber/Lyft ( ) Bicycle/Citibike ( ) Other: _______
- 18. • Bus Utility: 𝑉𝑏𝑢𝑠 = 𝛽 𝑛 𝑥 𝑛 • High significance of reliability factor and positive coefficient in the utility function ◦ Higher reliability decreases chance of switching from the bus • Travel time highly significant ◦ Walking time has the most impact when choosing to travel by bus 18 Mixed logit model β S Pr (>|z|) Intercept -6.512** 2.294 0.0045 Travel time -0.153*** 0.0453 0.0007 Reliability 0.590* 0.258 0.0223 Convenience - Obligatory 1.229 0.893 0.1689 SD (Travel time) 0.141* 0.057 0.0146
- 19. • More significant discrete choice model when including perception variables (latent factors of bus service) • Reliability factor is the most significant when choosing to travel by bus ◦ Frequency of service ◦ On-time performance ◦ Boarding time • Positive impact in utility of bus alternative: higher perception of bus service reliability implies less likelihood of switching from the bus • Efforts should be prioritized in improving reliability ◦ Traffic Signal Prioritizing for bus ◦ Minimize bunching of buses to minimize boarding time • Higher walking times have most negative impact in mode shift intention ◦ Optimize bus stops to minimize walking time without compromising bus speeds 19 Conclusions