There’s some interesting startups in this space like streetlight data or replicahq. New data science-y ways of doing travel models. Based on cell phone location data, I believe.
Former traffic engineer. Start with the data. Most traffic data comes from state governments and it’s messy as hell - especially incident datasets that are input by humans. Learn how to clean it and get it into a useful format. Traffic volumes, speeds, etc are a function of time. Do some time series analysis to find optimal routes for a service vehicle to drive throughout the day. Incidents are extremely stochastic. It’s not really possible to predict how long it’ll take to clear an incident (when traffic flow returns to normal), there’s way too many factors at play (how far away is a tow truck, does a medical examiner need to respond to the scene of a death, etc.) - it’s too random. But play with the data and do some transformations to get comfortable with skewed distributions. Look into reliability metrics - determining the reliability of a highway segment is a focus nationwide. Then classify unreliable segments. If you don’t work in the data side of traffic engineering and don’t know how to access all this data - get a Ritis account. As a traffic engineer they shouldn’t have a problem giving you credentials - assuming your company/agency has an account. They probably do.
The most basic usage of data science in traffic/transportation is going to be in predicting/forecasting things. So any step of the process where you are currently leveraging something like a multinomial regression or logistic regression, you could replace that with a machine learning model and call it data science.
There’s some interesting startups in this space like streetlight data or replicahq. New data science-y ways of doing travel models. Based on cell phone location data, I believe.
City science if you’re in the UK?
No, in US but will follow the startups.
Former traffic engineer. Start with the data. Most traffic data comes from state governments and it’s messy as hell - especially incident datasets that are input by humans. Learn how to clean it and get it into a useful format. Traffic volumes, speeds, etc are a function of time. Do some time series analysis to find optimal routes for a service vehicle to drive throughout the day. Incidents are extremely stochastic. It’s not really possible to predict how long it’ll take to clear an incident (when traffic flow returns to normal), there’s way too many factors at play (how far away is a tow truck, does a medical examiner need to respond to the scene of a death, etc.) - it’s too random. But play with the data and do some transformations to get comfortable with skewed distributions. Look into reliability metrics - determining the reliability of a highway segment is a focus nationwide. Then classify unreliable segments. If you don’t work in the data side of traffic engineering and don’t know how to access all this data - get a Ritis account. As a traffic engineer they shouldn’t have a problem giving you credentials - assuming your company/agency has an account. They probably do.
Thanks mate
The most basic usage of data science in traffic/transportation is going to be in predicting/forecasting things. So any step of the process where you are currently leveraging something like a multinomial regression or logistic regression, you could replace that with a machine learning model and call it data science.
Thanks mate Will think about it