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Adequate planning and management for freight transport are essential for attaining sustainable development in both transport and economy sectors alike. Locating and evaluating the optimal routes that minimize travel times and hence fuel consumption for delivery trucks are vital tasks to achieve such development. The central aim of this paper is to boost fresh food truck-based transport within the city of Al-Najaf (Iraq) by tackling the travel time reliability issue resulting from urban road congestion. Accordingly, two objectives need to be accomplished; first, to determine the fastest route for fresh produce (vegetables and fruits) delivery trucks traveling between the city's fresh food wholesale center and its southern entrance; second, to investigate the impacts of road traffic congestion on the optimal location for the least-time route. The methodology involves employing a passenger car equipped with a digital camera to conduct the required travel time and delay surveys using the floating car driving technique along the street network of Al-Najaf city. The collective length of the arterial and collector streets that have been surveyed is about 400 km. Six travel time runs have been carried out for each segment per direction at three traffic stream conditions; morning peak, off-peak, and free-flow conditions. A total of 3168 single travel time runs have been conducted. The ESRI ArcGIS (10.8.1) software is employed to digitize and model the network streets and thus conduct the required route-specific network analyses. A real case study that involves modeling and investigating the routes and driving times used by fresh food truck drivers traveling between Al-Najaf fresh food wholesale center and the city's southern entrance has been analyzed. The route analyses revealed that the ArcGIS-based fastest route can achieve a saving in driving time up to 25 minutes (66%) compared with other actual routes currently used by truck drivers. Furthermore, the analysis also revealed how the location of the resultant fastest route is highly influenced by traffic congestion. [ABSTRACT FROM AUTHOR]