{'fraction_non_alphanumeric': 0.015806988352745424, 'fraction_numerical': 0.0, 'mean_word_length': 5.398936170212766, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Decision support tools in transport planning: From research to practice

With the progress in transport demand modelling from aggregated, static and zonebased assignment models to fully dynamic agentand activity-based transport simulations, the scope of data that needs to be collected, managed and maintained has changed dramatically. Similar applies to the type of data that is now available to transport planners and decision makers as for example the emergence of public transport smart card data shows. The type of analyses as well as potential analysts have also changed. Besides transport planners, we see tremendous potential for additional end-users to access and analyse such data: urban planners, policy-makers and the service industry. In this paper, we present a framework of a decision support system designed to enable analysis of the wealth of information provided by agent-based transport demand models, travel diary surveys and automatically collected transportation data. We present a practical application of the framework using it for analyzing the MATSim model of Singapore in comparison with the Household Interview Travel Survey and public transport smart card transaction data.