Authors : Georgios N. Kouziokas
Type of publication : Academic article
Date of publication : 2016
Crime violence against citizens in urban environment has a negative impact on people’s lives and should be considered as an important factor in public transportation management and planning and in constructing the transportation infrastructure in urban areas. The development of communication and information technologies has led to the development of new technology-based management systems in public administration and also to new intelligent systems for transportation management in urban areas which facilitate decision making in choosing the optimal routes in public means of transport.
Crime risk prediction is examined as an important factor that contributes to safer travelling in urban areas in public transportation places where many people are gathered and must be protected efficiently by the police forces from crime committers.
Results and discussion
High crime risk transportation stations were predicted by using the optimum neural network prediction model. Firstly, the constructed model was tested by using the test data of the dataset for the last week of January 2013 in Chicago city. The results showed very precise prediction accuracy. The predicted crime hotspot areas where intersected with the transportation spatial layers regarding features about bus stops and rail stations. The final results showed the predicted transportation stations with high crime risk.
Artificial intelligence as an emerging forecasting technique was implemented in order to build the optimum neural network predictive model by investigating the most adequate network topologies and training parameters. The results showed a very good prediction accuracy of the transportation stations with high crime risk
Adopting artificial intelligence techniques and geographic information systems in public management and transportation can be very fruitful and valuable. An application example is this research, which applies geospatial technologies and artificial neural networks in order to predict efficiently the most dangerous public transportation stations in daily basis. High crime risk transportation stations were located by using spatial analysis methodologies in order to analyze the historical data of crime incidents of specific offenses which were negatively correlated to the transportation safety.
Artificial intelligence as an emerging forecasting technique was implemented in order to build the optimum neural network predictive model by investigating the most adequate network topologies and training parameters. The results showed a very good prediction accuracy of the transportation stations with high crime risk. That is very promising and can promote safer transportation management policies, especially in the cities where crime rates are very high.
Considering the number of people that are using the public means of transport, high risk stations predictions will help public administration to adopt planning and intervention strategies to ensure public safety. Obtaining spatial information about the crime distribution in transportation facilities, and especially predicting the crime occurrence – based high risk transportation stations can be very valuable for public management, urban planning and transportation management, and also in designing and planning proactive policies and strategies in order to protect people that are commuting with public means of transport and also to preserve human lives from criminal acts.
Les Wathinotes sont soit des résumés de publications sélectionnées par WATHI, conformes aux résumés originaux, soit des versions modifiées des résumés originaux, soit des extraits choisis par WATHI compte tenu de leur pertinence par rapport au thème du Débat. Lorsque les publications et leurs résumés ne sont disponibles qu’en français ou en anglais, WATHI se charge de la traduction des extraits choisis dans l’autre langue. Toutes les Wathinotes renvoient aux publications originales et intégrales qui ne sont pas hébergées par le site de WATHI, et sont destinées à promouvoir la lecture de ces documents, fruit du travail de recherche d’universitaires et d’experts.
The Wathinotes are either original abstracts of publications selected by WATHI, modified original summaries or publication quotes selected for their relevance for the theme of the Debate. When publications and abstracts are only available either in French or in English, the translation is done by WATHI. All the Wathinotes link to the original and integral publications that are not hosted on the WATHI website. WATHI participates to the promotion of these documents that have been written by university professors and experts.