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b'Dr Zhenliang Ma is a researcher and lecturer at Monash University and co-director of the graduate transport program jointly run by Monash University and Southeast University in China. Dr Ma moved to Monash after working at MIT, to join its interdisciplinary public research team. \\n\\nThis episode addresses the potential for data analytics to help transit agencies diagnose problems and identify opportunities to improve operations and customer satisfaction. Dr Ma provides some examples of problems that are suited to a data-driven solution, and some that aren\\u2019t. \\n\\n\\u201c[Data analytics] is used to try to transform data into information to derive insights, and from those insights, make better decisions.\\u201d\\n\\nHe characterises three particular applications of data analysis to transit problems: \\n1.\\tInferences problems, which leverage descriptive and diagnostic problems, which use travel data to understand system performance, and passenger decision making\\n2.\\tPrediction problems, which use predictive analysis to improve real time control of vehicles based on traffic conditions and disruption\\n3.\\tLong-term demand management problems, which use prescriptive analysis to test how users would respond to different incentives designed to change travel behaviour. \\n\\nWe discuss the application of prescriptive data analysis to address severe crowding in Hong Kong\\u2019s metro system (a similar solution to peak crowding is discussed in the Singapore context by Dr Waiyan Leong in Episode 7). This project sought to improve the payoff for demand management interventions by identifying users most likely to respond. \\n\\nDr Ma mentions a trial of a personalised incentive system for San Francisco\\u2019s Bay Area Transportation Authority (BART). The success rate of demand management improved for incentives targeted toward individuals rather than the station. Data analysis was used to understand behavioural responses to incentives, and to design the final demand management strategy to optimise success. \\n\\n\\u201cThe transportation system is very complex. By changing a small portion of the passengers behaviour, congestion will be solved\\u201d\\n\\nDr Ma defines three steps to tackle constrained public transport capacity during the COVID-19 pandemic. Highlighting this very deliberate approach to thinking about data science problems, he does so in the language of data science, proposing first to use data to describe usage patterns, diagnose problematic times, and predict what response might occur under different policy scenarios. Being deliberate in the way you approach the problem is key.\\n\\n\\u201cWe really need to think about how to represent our data, to tell the story or to understand the problem, and then we can develop new insights from that\\u201d\\n\\nHowever, although data analysis is useful for exploring a problem, it cannot explain why. Data-driven solutions alone are not enough to understand why human make decisions. \\n\\n\\u201cData science is just one of the tools, out of the set of tool that we can use to solve transport problems\\u201d\\n\\nWhat makes a great public transport data analyst? First, is an interest in data, and and open and sceptical mind (prepared to challenge results). Skills in programming, statistics and visualisation will give the aspiring data analyst a toolbox for their work. Finally and most importantly, is domain knowledge. \\n\\nFind Dr Ma\\u2019s publications and recommendations for upskilling in the full shownotes on our website: http://publictransportresearchgroup.info/?p=51742\\n\\nSing up for updates when we release shows: http://eepurl.com/g9tCdb\\n\\nMusic from this episode is from https://www.purple-planet.com'