Commuter rail systems, operating on unused or under-used railroad rights-of-way, are being introduced into many urban transportation systems. Since locations of available rail rights-of-way were typically chosen long ago to serve the needs of rail freight customers, the majority of commuter rail users do not live or work within walking distance of potential commuter rail stations. Minimizing access time to rail stations and final destinations is crucial if commuter rail is to be a viable option for commuters. This paper focuses on real time optimization of the Commuter Rail Circulator Route Network Design Problem (CRCNDP) supposing that real-time demand data can be obtained partially through users’ smart phone app. The route configuration of the circulator system – where to stop and the route among the stops – is determined on a real-time basis by employing adaptive Tabu Search to quickly solve a Mixed Integer Programming problem with an objective to minimize total cost incurred to both transit users and transit operators. Numerical experiments are executed and methodologies are proposed to find the threshold for the minimum fraction of travelers that would need to report their destinations via smart phone to guarantee the practical value of optimization based on real-time collected demand against a base case defined as the average performance of all possible routes.