Travel Behavior

Transportation mode recognition using GPS and accelerometer data

Cyclists

One of the big problems for smartphone travel diary apps is automatic mode detection. The split between walking and not is pretty easy, as is cycling, but what about separating cars from rail? Apps like Moves just dubs it "transport", but that doesn't help much with travel behavior research. A new paper in Transportarion Researc Part C examines using accelerometers and GPS to detect mode. Tao Feng and Harry J.P. Timmermans from Eindhoven University of Technology present their research in, "Transportation mode recognition using GPS and accelerometer data"

Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.

The full article can be found here

San Francisco Travel Quality Study

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A group of ITS Berkeley researchers need your help! The San Francisco Travel Quality Study is looking for participants right now. If you have an Android phone and use SF Muni, sign up!

What is the goal of the study? We want to understand how the quality of public transportation affects people’s choice of how to commute, and how Muni can best be improved to suit riders’ needs. This is an innovative study in which we want to get direct feedback from travelers. We are working with the SFMTA, so their voices will be heard! As a thank you, they receive a free Muni pass for a month!

Who can participate? Anybody who lives and works/goes to school in San Francisco; it doesn’t matter how they currently travel. Currently there is only an Android app available, but if resources permit, we might run an iPhone-based study early next year.

What does participation involve? The first round of the study runs from October 23 until December 7. Participants will be asked to install a survey app on their phones and use Muni on at least five days in November. They will then fill out the mobile mini-surveys (approx. 15 sec. each) for those days, plus three short online surveys (max. 10 min each) over the course of the six weeks of the study.

You can apply here.

Friday Seminar: Schools and Transport Emissions in the Six County Sacramento Region

Mystery Image (1983/232/13,195)

This week's TRANSOC Friday Seminar features Philine Gaffron, a visiting researcher at ITS UC Davis from Hamburg University of Technology. She will present Schools and Transport Emissions in the Six County Sacramento Region.

Environmental justice analyses in the transport field often look at people's exposure to transport emissions at their place of residence. This is both a vital angle as most people spend the majority of their time in and around their homes and it is also a proxy for studying overall exposure since significant amounts of time are spent elsewhere, particularly during the day, when traffic levels are highest. Schools are the most important 'elsewhere' for children and teenagers, who are also among the most vulnerable groups when it comes to the detrimental effects of transport emissions.
She will present the results of her analyses that look at the relationship between emission loads that schools in the Sacramento Area Council of Governments (SACOG) region are experiencing from road traffic and the socio-demographic make-up of their students. These results will further be compared to the findings of other studies investigating health and exposure in the SACOG region. Perhaps the discussion will highlight other work that might yield fruitful comparisons and it would also be interesting to discuss participants' opinions on and experiences with addressing inequalities in this area.

The seminar will take place Friday October 11, 2013 in 534 Davis Hall from 4:00-5:00 PM. Cookie Hour per usual will be in the library at 3:30 PM. 

Crowding in transit: How does it effects on riders, operations and demand.

SCRTD Crowded Bus Stop RTD_1131_13

Crowded bus stops and subway stations, which beget crowded buses and trains, are a part of riding transit. ITS Berkeley researchers are exploring how this effects rider attitudes

A new article from Transportation Research Part A: Policy and Practice examines this issue. In "Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand," researchers from Chile and Australia look at the effects of crowding on speed, waiting times, travel time reliability, and route choice. 

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.

The full article can be found here

Bad Moods and Risky Drivers

Road Rage*

There's a new article from Transportation Research Part F that explores a possible link between driver mood and risky driving. Researchers from Peking University's Department of Psychology tested to see how a good or bad mood could affect driving bevaior. From the paper, "Negative or positive? The effect of emotion and mood on risky driving"

This research explored how two states of affect, emotion and mood, would influence driver’s risky driving behavior through risk perception and risk attitude. An experiment and a survey were adopted to test the two paths. In this model, negative affect played an opposite and more powerful role compared to positive affect. Study 1 was an experimental study with four treatment groups. Participants watched one of four video clips (traffic-related negative, traffic-unrelated negative, positive and neutral) and different emotions were induced. Negative emotion significantly elevated drivers’ risk perception but such perception failed to develop an appropriate attitude for drivers. A more favorable risk attitude resulted in increased reports of speeding. Turning from a “point” effect to a similar “period” effect, a survey was carried out in Study 2 to explore the effect of positive and negative mood instead of emotion. Mood states affected drivers’ risky driving behavior through risk perception as well as risk attitude, which was in line with the results of Study 1. The “bad is stronger than good” effect and the two paths in the model were discussed.

The whole paper can be found here

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